WorldWideScience

Sample records for based fault detection

  1. Robust Mpc for Actuator–Fault Tolerance Using Set–Based Passive Fault Detection and Active Fault Isolation

    Directory of Open Access Journals (Sweden)

    Xu Feng

    2017-03-01

    Full Text Available In this paper, a fault-tolerant control (FTC scheme is proposed for actuator faults, which is built upon tube-based model predictive control (MPC as well as set-based fault detection and isolation (FDI. In the class of MPC techniques, tubebased MPC can effectively deal with system constraints and uncertainties with relatively low computational complexity compared with other robust MPC techniques such as min-max MPC. Set-based FDI, generally considering the worst case of uncertainties, can robustly detect and isolate actuator faults. In the proposed FTC scheme, fault detection (FD is passive by using invariant sets, while fault isolation (FI is active by means of MPC and tubes. The active FI method proposed in this paper is implemented by making use of the constraint-handling ability of MPC to manipulate the bounds of inputs.

  2. An Improved Wavelet‐Based Multivariable Fault Detection Scheme

    KAUST Repository

    Harrou, Fouzi

    2017-07-06

    Data observed from environmental and engineering processes are usually noisy and correlated in time, which makes the fault detection more difficult as the presence of noise degrades fault detection quality. Multiscale representation of data using wavelets is a powerful feature extraction tool that is well suited to denoising and decorrelating time series data. In this chapter, we combine the advantages of multiscale partial least squares (MSPLSs) modeling with those of the univariate EWMA (exponentially weighted moving average) monitoring chart, which results in an improved fault detection system, especially for detecting small faults in highly correlated, multivariate data. Toward this end, we applied EWMA chart to the output residuals obtained from MSPLS model. It is shown through simulated distillation column data the significant improvement in fault detection can be obtained by using the proposed methods as compared to the use of the conventional partial least square (PLS)‐based Q and EWMA methods and MSPLS‐based Q method.

  3. Risk-based fault detection using Self-Organizing Map

    International Nuclear Information System (INIS)

    Yu, Hongyang; Khan, Faisal; Garaniya, Vikram

    2015-01-01

    The complexity of modern systems is increasing rapidly and the dominating relationships among system variables have become highly non-linear. This results in difficulty in the identification of a system's operating states. In turn, this difficulty affects the sensitivity of fault detection and imposes a challenge on ensuring the safety of operation. In recent years, Self-Organizing Maps has gained popularity in system monitoring as a robust non-linear dimensionality reduction tool. Self-Organizing Map is able to capture non-linear variations of the system. Therefore, it is sensitive to the change of a system's states leading to early detection of fault. In this paper, a new approach based on Self-Organizing Map is proposed to detect and assess the risk of fault. In addition, probabilistic analysis is applied to characterize the risk of fault into different levels according to the hazard potential to enable a refined monitoring of the system. The proposed approach is applied on two experimental systems. The results from both systems have shown high sensitivity of the proposed approach in detecting and identifying the root cause of faults. The refined monitoring facilitates the determination of the risk of fault and early deployment of remedial actions and safety measures to minimize the potential impact of fault. - Highlights: • A new approach based on Self-Organizing Map is proposed to detect faults. • Integration of fault detection with risk assessment methodology. • Fault risk characterization into different levels to enable focused system monitoring

  4. Nonlinear Model-Based Fault Detection for a Hydraulic Actuator

    NARCIS (Netherlands)

    Van Eykeren, L.; Chu, Q.P.

    2011-01-01

    This paper presents a model-based fault detection algorithm for a specific fault scenario of the ADDSAFE project. The fault considered is the disconnection of a control surface from its hydraulic actuator. Detecting this type of fault as fast as possible helps to operate an aircraft more cost

  5. Analytical Model-based Fault Detection and Isolation in Control Systems

    DEFF Research Database (Denmark)

    Vukic, Z.; Ozbolt, H.; Blanke, M.

    1998-01-01

    The paper gives an introduction and an overview of the field of fault detection and isolation for control systems. The summary of analytical (quantitative model-based) methodds and their implementation are presented. The focus is given to mthe analytical model-based fault-detection and fault...

  6. Gear-box fault detection using time-frequency based methods

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Stoustrup, Jakob

    2015-01-01

    Gear-box fault monitoring and detection is important for optimization of power generation and availability of wind turbines. The current industrial approach is to use condition monitoring systems, which runs in parallel with the wind turbine control system, using expensive additional sensors...... in the gear-box resonance frequency can be detected. Two different time–frequency based approaches are presented in this paper. One is a filter based approach and the other is based on a Karhunen–Loeve basis. Both of them detect the gear-box fault with an acceptable detection delay of maximum 100s, which...... is neglectable compared with the fault developing time....

  7. Active Fault Detection Based on a Statistical Test

    DEFF Research Database (Denmark)

    Sekunda, André Krabdrup; Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2016-01-01

    In this paper active fault detection of closed loop systems using dual Youla-Jabr-Bongiorno-Kucera(YJBK) parameters is presented. Until now all detector design for active fault detection using the dual YJBK parameters has been based on CUSUM detectors. Here a method for design of a matched filter...

  8. Exact, almost and delayed fault detection: An observer based approach

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Saberi, Ali; Stoorvogel, Anton A.

    1999-01-01

    This paper consider the problem of fault detection and isolation in continuous- and discrete-time systems while using zero or almost zero threshold. A number of different fault detections and isolation problems using exact or almost exact disturbance decoupling are formulated. Solvability...... conditions are given for the formulated design problems together with methods for appropriate design of observer based fault detectors. The l-step delayed fault detection problem is also considered for discrete-time systems . Moreover, certain indirect fault detection methods such as unknown input observers...

  9. Nonlinear observer based fault detection and isolation for a momentum wheel

    DEFF Research Database (Denmark)

    Jensen, Hans-Christian Becker; Wisniewski, Rafal

    2001-01-01

    This article realizes nonlinear Fault Detection and Isolation for a momentum wheel. The Fault Detection and Isolation is based on a Failure Mode and Effect Analysis, which states which faults might occur and can be detected. The algorithms presented in this paper are based on a geometric approach...... toachieve nonlinear Fault Detection and Isolation. The proposed algorithms are tested in a simulation study and the pros and cons of the algorithm are discussed....

  10. Reset Tree-Based Optical Fault Detection

    Directory of Open Access Journals (Sweden)

    Howon Kim

    2013-05-01

    Full Text Available In this paper, we present a new reset tree-based scheme to protect cryptographic hardware against optical fault injection attacks. As one of the most powerful invasive attacks on cryptographic hardware, optical fault attacks cause semiconductors to misbehave by injecting high-energy light into a decapped integrated circuit. The contaminated result from the affected chip is then used to reveal secret information, such as a key, from the cryptographic hardware. Since the advent of such attacks, various countermeasures have been proposed. Although most of these countermeasures are strong, there is still the possibility of attack. In this paper, we present a novel optical fault detection scheme that utilizes the buffers on a circuit’s reset signal tree as a fault detection sensor. To evaluate our proposal, we model radiation-induced currents into circuit components and perform a SPICE simulation. The proposed scheme is expected to be used as a supplemental security tool.

  11. Fault detection and reliability, knowledge based and other approaches

    International Nuclear Information System (INIS)

    Singh, M.G.; Hindi, K.S.; Tzafestas, S.G.

    1987-01-01

    These proceedings are split up into four major parts in order to reflect the most significant aspects of reliability and fault detection as viewed at present. The first part deals with knowledge-based systems and comprises eleven contributions from leading experts in the field. The emphasis here is primarily on the use of artificial intelligence, expert systems and other knowledge-based systems for fault detection and reliability. The second part is devoted to fault detection of technological systems and comprises thirteen contributions dealing with applications of fault detection techniques to various technological systems such as gas networks, electric power systems, nuclear reactors and assembly cells. The third part of the proceedings, which consists of seven contributions, treats robust, fault tolerant and intelligent controllers and covers methodological issues as well as several applications ranging from nuclear power plants to industrial robots to steel grinding. The fourth part treats fault tolerant digital techniques and comprises five contributions. Two papers, one on reactor noise analysis, the other on reactor control system design, are indexed separately. (author)

  12. Fault detection and isolation in systems with parametric faults

    DEFF Research Database (Denmark)

    Stoustrup, Jakob; Niemann, Hans Henrik

    1999-01-01

    The problem of fault detection and isolation of parametric faults is considered in this paper. A fault detection problem based on parametric faults are associated with internal parameter variations in the dynamical system. A fault detection and isolation method for parametric faults is formulated...

  13. Fault detection and fault tolerant control of a smart base isolation system with magneto-rheological damper

    International Nuclear Information System (INIS)

    Wang, Han; Song, Gangbing

    2011-01-01

    Fault detection and isolation (FDI) in real-time systems can provide early warnings for faulty sensors and actuator signals to prevent events that lead to catastrophic failures. The main objective of this paper is to develop FDI and fault tolerant control techniques for base isolation systems with magneto-rheological (MR) dampers. Thus, this paper presents a fixed-order FDI filter design procedure based on linear matrix inequalities (LMI). The necessary and sufficient conditions for the existence of a solution for detecting and isolating faults using the H ∞ formulation is provided in the proposed filter design. Furthermore, an FDI-filter-based fuzzy fault tolerant controller (FFTC) for a base isolation structure model was designed to preserve the pre-specified performance of the system in the presence of various unknown faults. Simulation and experimental results demonstrated that the designed filter can successfully detect and isolate faults from displacement sensors and accelerometers while maintaining excellent performance of the base isolation technology under faulty conditions

  14. Model Based Fault Detection in a Centrifugal Pump Application

    DEFF Research Database (Denmark)

    Kallesøe, Carsten; Cocquempot, Vincent; Izadi-Zamanabadi, Roozbeh

    2006-01-01

    A model based approach for fault detection in a centrifugal pump, driven by an induction motor, is proposed in this paper. The fault detection algorithm is derived using a combination of structural analysis, observer design and Analytical Redundancy Relation (ARR) design. Structural considerations...

  15. Observer-based Fault Detection and Isolation for Nonlinear Systems

    DEFF Research Database (Denmark)

    Lootsma, T.F.

    With the rise in automation the increase in fault detectionand isolation & reconfiguration is inevitable. Interest in fault detection and isolation (FDI) for nonlinear systems has grown significantly in recent years. The design of FDI is motivated by the need for knowledge about occurring faults...... in fault-tolerant control systems (FTC systems). The idea of FTC systems is to detect, isolate, and handle faults in such a way that the systems can still perform in a required manner. One prefers reduced performance after occurrence of a fault to the shut down of (sub-) systems. Hence, the idea of fault......-output decoupling is described. It is a new idea based on the solution of the input-output decoupling problem. The idea is to include FDI considerations already during the control design....

  16. A New Acoustic Emission Sensor Based Gear Fault Detection Approach

    Directory of Open Access Journals (Sweden)

    Junda Zhu

    2013-01-01

    Full Text Available In order to reduce wind energy costs, prognostics and health management (PHM of wind turbine is needed to ensure the reliability and availability of wind turbines. A gearbox is an important component of a wind turbine. Therefore, developing effective gearbox fault detection tools is important to the PHM of wind turbine. In this paper, a new acoustic emission (AE sensor based gear fault detection approach is presented. This approach combines a heterodyne based frequency reduction technique with time synchronous average (TSA and spectrum kurtosis (SK to process AE sensor signals and extract features as condition indictors for gear fault detection. Heterodyne technique commonly used in communication is first employed to preprocess the AE signals before sampling. By heterodyning, the AE signal frequency is down shifted from several hundred kHz to below 50 kHz. This reduced AE signal sampling rate is comparable to that of vibration signals. The presented approach is validated using seeded gear tooth crack fault tests on a notational split torque gearbox. The approach presented in this paper is physics based and the validation results have showed that it could effectively detect the gear faults.

  17. Wavelet Packet based Detection of Surface Faults on Compact Discs

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Stoustrup, Jakob; Wickerhauser, Mladen Victor

    2006-01-01

    based on these measurements. A precise detection of the surface fault is a prerequisite to a correct handling of the faults in order to protect the pick-up of the compact disc player from audible track losses. The actual fault handling which is addressed in other publications can be carried out......In this paper the detection of faults on the surface of a compact disc is addressed. Surface faults like scratches and fingerprints disturb the on-line measurement of the pick-up position relative to the track. This is critical since the pick-up is focused on and tracked at the information track...

  18. ASCS online fault detection and isolation based on an improved MPCA

    Science.gov (United States)

    Peng, Jianxin; Liu, Haiou; Hu, Yuhui; Xi, Junqiang; Chen, Huiyan

    2014-09-01

    Multi-way principal component analysis (MPCA) has received considerable attention and been widely used in process monitoring. A traditional MPCA algorithm unfolds multiple batches of historical data into a two-dimensional matrix and cut the matrix along the time axis to form subspaces. However, low efficiency of subspaces and difficult fault isolation are the common disadvantages for the principal component model. This paper presents a new subspace construction method based on kernel density estimation function that can effectively reduce the storage amount of the subspace information. The MPCA model and the knowledge base are built based on the new subspace. Then, fault detection and isolation with the squared prediction error (SPE) statistic and the Hotelling ( T 2) statistic are also realized in process monitoring. When a fault occurs, fault isolation based on the SPE statistic is achieved by residual contribution analysis of different variables. For fault isolation of subspace based on the T 2 statistic, the relationship between the statistic indicator and state variables is constructed, and the constraint conditions are presented to check the validity of fault isolation. Then, to improve the robustness of fault isolation to unexpected disturbances, the statistic method is adopted to set the relation between single subspace and multiple subspaces to increase the corrective rate of fault isolation. Finally fault detection and isolation based on the improved MPCA is used to monitor the automatic shift control system (ASCS) to prove the correctness and effectiveness of the algorithm. The research proposes a new subspace construction method to reduce the required storage capacity and to prove the robustness of the principal component model, and sets the relationship between the state variables and fault detection indicators for fault isolation.

  19. Improved Data-based Fault Detection Strategy and Application to Distillation Columns

    KAUST Repository

    Madakyaru, Muddu

    2017-01-31

    Chemical and petrochemical processes require continuous monitoring to detect abnormal events and to sustain normal operations. Furthermore, process monitoring enhances productivity, efficiency, and safety in process industries. Here, we propose an innovative statistical approach that exploits the advantages of multiscale partial least squares (MSPLS) models and generalized likelihood ratio (GLR) tests for fault detection in processes. Specifically, we combine an MSPLS algorithm with wavelet analysis to create our modeling framework. Then, we use GLR hypothesis testing based on the uncorrelated residuals obtained from the MSPLS model to improve fault detection. We use simulated distillation column data to evaluate the MSPLS-based GLR chart. Results show that our MSPLS-based GLR method is more powerful than the PLS-based Q and GLR method and MSPLS-based Q method, especially in early detection of small faults with abrupt or incipient behavior.

  20. Improved Data-based Fault Detection Strategy and Application to Distillation Columns

    KAUST Repository

    Madakyaru, Muddu; Harrou, Fouzi; Sun, Ying

    2017-01-01

    Chemical and petrochemical processes require continuous monitoring to detect abnormal events and to sustain normal operations. Furthermore, process monitoring enhances productivity, efficiency, and safety in process industries. Here, we propose an innovative statistical approach that exploits the advantages of multiscale partial least squares (MSPLS) models and generalized likelihood ratio (GLR) tests for fault detection in processes. Specifically, we combine an MSPLS algorithm with wavelet analysis to create our modeling framework. Then, we use GLR hypothesis testing based on the uncorrelated residuals obtained from the MSPLS model to improve fault detection. We use simulated distillation column data to evaluate the MSPLS-based GLR chart. Results show that our MSPLS-based GLR method is more powerful than the PLS-based Q and GLR method and MSPLS-based Q method, especially in early detection of small faults with abrupt or incipient behavior.

  1. EKF-based fault detection for guided missiles flight control system

    Science.gov (United States)

    Feng, Gang; Yang, Zhiyong; Liu, Yongjin

    2017-03-01

    The guided missiles flight control system is essential for guidance accuracy and kill probability. It is complicated and fragile. Since actuator faults and sensor faults could seriously affect the security and reliability of the system, fault detection for missiles flight control system is of great significance. This paper deals with the problem of fault detection for the closed-loop nonlinear model of the guided missiles flight control system in the presence of disturbance. First, set up the fault model of flight control system, and then design the residual generation based on the extended Kalman filter (EKF) for the Eulerian-discrete fault model. After that, the Chi-square test was selected for the residual evaluation and the fault detention task for guided missiles closed-loop system was accomplished. Finally, simulation results are provided to illustrate the effectiveness of the approach proposed in the case of elevator fault separately.

  2. Observer Based Fault Detection and Moisture Estimating in Coal Mill

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Mataji, Babak

    2008-01-01

    In this paper an observer-based method for detecting faults and estimating moisture content in the coal in coal mills is presented. Handling of faults and operation under special conditions, such as high moisture content in the coal, are of growing importance due to the increasing...... requirements to the general performance of power plants. Detection  of faults and moisture content estimation are consequently of high interest in the handling of the problems caused by faults and moisture content. The coal flow out of the mill is the obvious variable to monitor, when detecting non-intended drops in the coal...... flow out of the coal mill. However, this variable is not measurable. Another estimated variable is the moisture content, which is only "measurable" during steady-state operations of the coal mill. Instead, this paper suggests a method where these unknown variables are estimated based on a simple energy...

  3. Fuzzy model-based observers for fault detection in CSTR.

    Science.gov (United States)

    Ballesteros-Moncada, Hazael; Herrera-López, Enrique J; Anzurez-Marín, Juan

    2015-11-01

    Under the vast variety of fuzzy model-based observers reported in the literature, what would be the properone to be used for fault detection in a class of chemical reactor? In this study four fuzzy model-based observers for sensor fault detection of a Continuous Stirred Tank Reactor were designed and compared. The designs include (i) a Luenberger fuzzy observer, (ii) a Luenberger fuzzy observer with sliding modes, (iii) a Walcott-Zak fuzzy observer, and (iv) an Utkin fuzzy observer. A negative, an oscillating fault signal, and a bounded random noise signal with a maximum value of ±0.4 were used to evaluate and compare the performance of the fuzzy observers. The Utkin fuzzy observer showed the best performance under the tested conditions. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  4. Fault Detection, Isolation, and Accommodation for LTI Systems Based on GIMC Structure

    Directory of Open Access Journals (Sweden)

    D. U. Campos-Delgado

    2008-01-01

    Full Text Available In this contribution, an active fault-tolerant scheme that achieves fault detection, isolation, and accommodation is developed for LTI systems. Faults and perturbations are considered as additive signals that modify the state or output equations. The accommodation scheme is based on the generalized internal model control architecture recently proposed for fault-tolerant control. In order to improve the performance after a fault, the compensation is considered in two steps according with a fault detection and isolation algorithm. After a fault scenario is detected, a general fault compensator is activated. Finally, once the fault is isolated, a specific compensator is introduced. In this setup, multiple faults could be treated simultaneously since their effect is additive. Design strategies for a nominal condition and under model uncertainty are presented in the paper. In addition, performance indices are also introduced to evaluate the resulting fault-tolerant scheme for detection, isolation, and accommodation. Hard thresholds are suggested for detection and isolation purposes, meanwhile, adaptive ones are considered under model uncertainty to reduce the conservativeness. A complete simulation evaluation is carried out for a DC motor setup.

  5. Evaluation of Wind Farm Controller based Fault Detection and Isolation

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Shafiei, Seyed Ehsan

    2015-01-01

    detection and isolation and fault tolerant control has previously been proposed. Based on this model, and international competition on wind farm FDI was organized. The contributions were presented at the IFAC World Congress 2014. In this paper the top three contributions to this competition are shortly......In the process of lowering cost of energy of power generated by wind turbines, some focus has been drawn towards fault detection and isolation and as well as fault tolerant control of wind turbines with the purpose of increasing reliability and availability of the wind turbines. Most modern wind...

  6. Active fault detection in MIMO systems

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2014-01-01

    The focus in this paper is on active fault detection (AFD) for MIMO systems with parametric faults. The problem of design of auxiliary inputs with respect to detection of parametric faults is investigated. An analysis of the design of auxiliary inputs is given based on analytic transfer functions...... from auxiliary input to residual outputs. The analysis is based on a singular value decomposition of these transfer functions Based on this analysis, it is possible to design auxiliary input as well as design of the associated residual vector with respect to every single parametric fault in the system...... such that it is possible to detect these faults....

  7. Gear Fault Detection Based on Teager-Huang Transform

    Directory of Open Access Journals (Sweden)

    Hui Li

    2010-01-01

    Full Text Available Gear fault detection based on Empirical Mode Decomposition (EMD and Teager Kaiser Energy Operator (TKEO technique is presented. This novel method is named as Teager-Huang transform (THT. EMD can adaptively decompose the vibration signal into a series of zero mean Intrinsic Mode Functions (IMFs. TKEO can track the instantaneous amplitude and instantaneous frequency of the Intrinsic Mode Functions at any instant. The experimental results provide effective evidence that Teager-Huang transform has better resolution than that of Hilbert-Huang transform. The Teager-Huang transform can effectively diagnose the fault of the gear, thus providing a viable processing tool for gearbox defect detection and diagnosis.

  8. Frequency Based Fault Detection in Wind Turbines

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Stoustrup, Jakob

    2014-01-01

    In order to obtain lower cost of energy for wind turbines fault detection and accommodation is important. Expensive condition monitoring systems are often used to monitor the condition of rotating and vibrating system parts. One example is the gearbox in a wind turbine. This system is operated...... in parallel to the control system, using different computers and additional often expensive sensors. In this paper a simple filter based algorithm is proposed to detect changes in a resonance frequency in a system, exemplified with faults resulting in changes in the resonance frequency in the wind turbine...... gearbox. Only the generator speed measurement which is available in even simple wind turbine control systems is used as input. Consequently this proposed scheme does not need additional sensors and computers for monitoring the condition of the wind gearbox. The scheme is evaluated on a wide-spread wind...

  9. An Improved Wavelet‐Based Multivariable Fault Detection Scheme

    KAUST Repository

    Harrou, Fouzi; Sun, Ying; Madakyaru, Muddu

    2017-01-01

    Data observed from environmental and engineering processes are usually noisy and correlated in time, which makes the fault detection more difficult as the presence of noise degrades fault detection quality. Multiscale representation of data using

  10. Fault detection of Tennessee Eastman process based on topological features and SVM

    Science.gov (United States)

    Zhao, Huiyang; Hu, Yanzhu; Ai, Xinbo; Hu, Yu; Meng, Zhen

    2018-03-01

    Fault detection in industrial process is a popular research topic. Although the distributed control system(DCS) has been introduced to monitor the state of industrial process, it still cannot satisfy all the requirements for fault detection of all the industrial systems. In this paper, we proposed a novel method based on topological features and support vector machine(SVM), for fault detection of industrial process. The proposed method takes global information of measured variables into account by complex network model and predicts whether a system has generated some faults or not by SVM. The proposed method can be divided into four steps, i.e. network construction, network analysis, model training and model testing respectively. Finally, we apply the model to Tennessee Eastman process(TEP). The results show that this method works well and can be a useful supplement for fault detection of industrial process.

  11. Model-Based Fault Detection and Isolation of a Liquid-Cooled Frequency Converter on a Wind Turbine

    DEFF Research Database (Denmark)

    Li, Peng; Odgaard, Peter Fogh; Stoustrup, Jakob

    2012-01-01

    advanced fault detection and isolation schemes. In this paper, an observer-based fault detection and isolation method for the cooling system in a liquid-cooled frequency converter on a wind turbine which is built up in a scalar version in the laboratory is presented. A dynamic model of the scale cooling...... system is derived based on energy balance equation. A fault analysis is conducted to determine the severity and occurrence rate of possible component faults and their end effects in the cooling system. A method using unknown input observer is developed in order to detect and isolate the faults based...... on the developed dynamical model. The designed fault detection and isolation algorithm is applied on a set of measured experiment data in which different faults are artificially introduced to the scaled cooling system. The experimental results conclude that the different faults are successfully detected...

  12. Fault Detection for Large-Scale Railway Maintenance Equipment Base on Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Junfu Yu

    2014-04-01

    Full Text Available Focusing on the fault detection application for large-scale railway maintenance equipment with the specialties of low-cost, energy efficiency, collecting data of the function units. This paper proposed energy efficiency, convenient installation fault detection application using Sigsbee wireless sensor networks, which Sigsbee is the most widely used protocol based on IEEE 802.15.4. This paper proposed a systematic application from hardware design using STM32F103 chips as processer, to software system. Fault detection application is the basic part of the fault diagnose system, wireless sensor nodes of the fault detection application with different kinds of sensors for verities function units communication by Sigsbee to collecting and sending basic working status data to the home gateway, then data will be sent to the fault diagnose system.

  13. Transient Monitoring Function–Based Fault Detection for Inverter-Interfaced Microgrids

    DEFF Research Database (Denmark)

    Sadeghkhani, Iman; Esmail Hamedani Golshan, Mohamad; Mehrizi-Sani, Ali

    2018-01-01

    One of the major challenges in protection of the inverter-interfaced islanded microgrids is their limited fault current level. This degrades the performance of traditional overcurrent protection schemes. This paper proposes a fault detection strategy based on monitoring the transient response......-domain simulation case studies using the CIGRE benchmark low voltage microgrid network....

  14. Model based Fault Detection and Isolation for Driving Motors of a Ground Vehicle

    Directory of Open Access Journals (Sweden)

    Young-Joon Kim

    2016-04-01

    Full Text Available This paper proposes model based current sensor and position sensor fault detection and isolation algorithm for driving motor of In-wheel independent drive electric vehicle. From low level perspective, fault diagnosis conducted and analyzed to enhance robustness and stability. Composing state equation of interior permanent magnet synchronous motor (IPMSM, current sensor fault and position sensor fault diagnosed with parity equation. Validation and usefulness of algorithm confirmed based on IPMSM fault occurrence simulation data.

  15. Fault Detection and Isolation for Spacecraft

    DEFF Research Database (Denmark)

    Jensen, Hans-Christian Becker; Wisniewski, Rafal

    2002-01-01

    This article realizes nonlinear Fault Detection and Isolation for actuators, given there is no measurement of the states in the actuators. The Fault Detection and Isolation of the actuators is instead based on angular velocity measurement of the spacecraft and knowledge about the dynamics...... of the satellite. The algorithms presented in this paper are based on a geometric approach to achieve nonlinear Fault Detection and Isolation. The proposed algorithms are tested in a simulation study and the pros and cons of the algorithms are discussed....

  16. Online model-based fault detection for grid connected PV systems monitoring

    KAUST Repository

    Harrou, Fouzi; Sun, Ying; Saidi, Ahmed

    2017-01-01

    This paper presents an efficient fault detection approach to monitor the direct current (DC) side of photovoltaic (PV) systems. The key contribution of this work is combining both single diode model (SDM) flexibility and the cumulative sum (CUSUM) chart efficiency to detect incipient faults. In fact, unknown electrical parameters of SDM are firstly identified using an efficient heuristic algorithm, named Artificial Bee Colony algorithm. Then, based on the identified parameters, a simulation model is built and validated using a co-simulation between Matlab/Simulink and PSIM. Next, the peak power (Pmpp) residuals of the entire PV array are generated based on both real measured and simulated Pmpp values. Residuals are used as the input for the CUSUM scheme to detect potential faults. We validate the effectiveness of this approach using practical data from an actual 20 MWp grid-connected PV system located in the province of Adrar, Algeria.

  17. Online model-based fault detection for grid connected PV systems monitoring

    KAUST Repository

    Harrou, Fouzi

    2017-12-14

    This paper presents an efficient fault detection approach to monitor the direct current (DC) side of photovoltaic (PV) systems. The key contribution of this work is combining both single diode model (SDM) flexibility and the cumulative sum (CUSUM) chart efficiency to detect incipient faults. In fact, unknown electrical parameters of SDM are firstly identified using an efficient heuristic algorithm, named Artificial Bee Colony algorithm. Then, based on the identified parameters, a simulation model is built and validated using a co-simulation between Matlab/Simulink and PSIM. Next, the peak power (Pmpp) residuals of the entire PV array are generated based on both real measured and simulated Pmpp values. Residuals are used as the input for the CUSUM scheme to detect potential faults. We validate the effectiveness of this approach using practical data from an actual 20 MWp grid-connected PV system located in the province of Adrar, Algeria.

  18. Observer Based Detection of Sensor Faults in Wind Turbines

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Stoustrup, Jakob; Nielsen, R.

    2009-01-01

    , if an unknown input observer the fault detection  scheme can be non dependent on the actual wind speed. The scheme  is validated on data from a more advanced and detailed simulation  model. The proposed scheme detects the sensor faults a few samples  after the beginning of the faults....

  19. Induction machine bearing faults detection based on a multi-dimensional MUSIC algorithm and maximum likelihood estimation.

    Science.gov (United States)

    Elbouchikhi, Elhoussin; Choqueuse, Vincent; Benbouzid, Mohamed

    2016-07-01

    Condition monitoring of electric drives is of paramount importance since it contributes to enhance the system reliability and availability. Moreover, the knowledge about the fault mode behavior is extremely important in order to improve system protection and fault-tolerant control. Fault detection and diagnosis in squirrel cage induction machines based on motor current signature analysis (MCSA) has been widely investigated. Several high resolution spectral estimation techniques have been developed and used to detect induction machine abnormal operating conditions. This paper focuses on the application of MCSA for the detection of abnormal mechanical conditions that may lead to induction machines failure. In fact, this paper is devoted to the detection of single-point defects in bearings based on parametric spectral estimation. A multi-dimensional MUSIC (MD MUSIC) algorithm has been developed for bearing faults detection based on bearing faults characteristic frequencies. This method has been used to estimate the fundamental frequency and the fault related frequency. Then, an amplitude estimator of the fault characteristic frequencies has been proposed and fault indicator has been derived for fault severity measurement. The proposed bearing faults detection approach is assessed using simulated stator currents data, issued from a coupled electromagnetic circuits approach for air-gap eccentricity emulating bearing faults. Then, experimental data are used for validation purposes. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  20. Fault Detection for Industrial Processes

    Directory of Open Access Journals (Sweden)

    Yingwei Zhang

    2012-01-01

    Full Text Available A new fault-relevant KPCA algorithm is proposed. Then the fault detection approach is proposed based on the fault-relevant KPCA algorithm. The proposed method further decomposes both the KPCA principal space and residual space into two subspaces. Compared with traditional statistical techniques, the fault subspace is separated based on the fault-relevant influence. This method can find fault-relevant principal directions and principal components of systematic subspace and residual subspace for process monitoring. The proposed monitoring approach is applied to Tennessee Eastman process and penicillin fermentation process. The simulation results show the effectiveness of the proposed method.

  1. Fault Detection for Automotive Shock Absorber

    Science.gov (United States)

    Hernandez-Alcantara, Diana; Morales-Menendez, Ruben; Amezquita-Brooks, Luis

    2015-11-01

    Fault detection for automotive semi-active shock absorbers is a challenge due to the non-linear dynamics and the strong influence of the disturbances such as the road profile. First obstacle for this task, is the modeling of the fault, which has been shown to be of multiplicative nature. Many of the most widespread fault detection schemes consider additive faults. Two model-based fault algorithms for semiactive shock absorber are compared: an observer-based approach and a parameter identification approach. The performance of these schemes is validated and compared using a commercial vehicle model that was experimentally validated. Early results shows that a parameter identification approach is more accurate, whereas an observer-based approach is less sensible to parametric uncertainty.

  2. Observer and data-driven model based fault detection in Power Plant Coal Mills

    DEFF Research Database (Denmark)

    Fogh Odgaard, Peter; Lin, Bao; Jørgensen, Sten Bay

    2008-01-01

    model with motor power as the controlled variable, data-driven methods for fault detection are also investigated. Regression models that represent normal operating conditions (NOCs) are developed with both static and dynamic principal component analysis and partial least squares methods. The residual...... between process measurement and the NOC model prediction is used for fault detection. A hybrid approach, where a data-driven model is employed to derive an optimal unknown input observer, is also implemented. The three methods are evaluated with case studies on coal mill data, which includes a fault......This paper presents and compares model-based and data-driven fault detection approaches for coal mill systems. The first approach detects faults with an optimal unknown input observer developed from a simplified energy balance model. Due to the time-consuming effort in developing a first principles...

  3. Reliable fault detection and diagnosis of photovoltaic systems based on statistical monitoring approaches

    KAUST Repository

    Harrou, Fouzi

    2017-09-18

    This study reports the development of an innovative fault detection and diagnosis scheme to monitor the direct current (DC) side of photovoltaic (PV) systems. Towards this end, we propose a statistical approach that exploits the advantages of one-diode model and those of the univariate and multivariate exponentially weighted moving average (EWMA) charts to better detect faults. Specifically, we generate array\\'s residuals of current, voltage and power using measured temperature and irradiance. These residuals capture the difference between the measurements and the predictions MPP for the current, voltage and power from the one-diode model, and use them as fault indicators. Then, we apply the multivariate EWMA (MEWMA) monitoring chart to the residuals to detect faults. However, a MEWMA scheme cannot identify the type of fault. Once a fault is detected in MEWMA chart, the univariate EWMA chart based on current and voltage indicators is used to identify the type of fault (e.g., short-circuit, open-circuit and shading faults). We applied this strategy to real data from the grid-connected PV system installed at the Renewable Energy Development Center, Algeria. Results show the capacity of the proposed strategy to monitors the DC side of PV systems and detects partial shading.

  4. Wind Turbine Fault Detection based on Artificial Neural Network Analysis of SCADA Data

    DEFF Research Database (Denmark)

    Herp, Jürgen; S. Nadimi, Esmaeil

    2015-01-01

    Slowly developing faults in wind turbine can, when not detected and fixed on time, cause severe damage and downtime. We are proposing a fault detection method based on Artificial Neural Networks (ANN) and the recordings from Supervisory Control and Data Acquisition (SCADA) systems installed in wind...

  5. Fault Detection for a Diesel Engine Actuator

    DEFF Research Database (Denmark)

    Blanke, M.; Bøgh, S.A.; Jørgensen, R.B.

    1995-01-01

    An electro-mechanical position servo is introduced as a benchmark for mode-based Fault Detection and Identification (FDI).......An electro-mechanical position servo is introduced as a benchmark for mode-based Fault Detection and Identification (FDI)....

  6. Model-based fault detection algorithm for photovoltaic system monitoring

    KAUST Repository

    Harrou, Fouzi

    2018-02-12

    Reliable detection of faults in PV systems plays an important role in improving their reliability, productivity, and safety. This paper addresses the detection of faults in the direct current (DC) side of photovoltaic (PV) systems using a statistical approach. Specifically, a simulation model that mimics the theoretical performances of the inspected PV system is designed. Residuals, which are the difference between the measured and estimated output data, are used as a fault indicator. Indeed, residuals are used as the input for the Multivariate CUmulative SUM (MCUSUM) algorithm to detect potential faults. We evaluated the proposed method by using data from an actual 20 MWp grid-connected PV system located in the province of Adrar, Algeria.

  7. Model-based fault detection algorithm for photovoltaic system monitoring

    KAUST Repository

    Harrou, Fouzi; Sun, Ying; Saidi, Ahmed

    2018-01-01

    Reliable detection of faults in PV systems plays an important role in improving their reliability, productivity, and safety. This paper addresses the detection of faults in the direct current (DC) side of photovoltaic (PV) systems using a

  8. Unknown input observer based detection of sensor faults in a wind turbine

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Stoustrup, Jakob

    2010-01-01

    In this paper an unknown input observer is designed to detect three different sensor fault scenarios in a specified bench mark model for fault detection and accommodation of wind turbines. In this paper a subset of faults is dealt with, it are faults in the rotor and generator speed sensors as well...... as a converter sensor fault. The proposed scheme detects the speed sensor faults in question within the specified requirements given in the bench mark model, while the converter fault is detected but not within the required time to detect....

  9. Improved nonlinear fault detection strategy based on the Hellinger distance metric: Plug flow reactor monitoring

    KAUST Repository

    Harrou, Fouzi

    2017-03-18

    Fault detection has a vital role in the process industry to enhance productivity, efficiency, and safety, and to avoid expensive maintenance. This paper proposes an innovative multivariate fault detection method that can be used for monitoring nonlinear processes. The proposed method merges advantages of nonlinear projection to latent structures (NLPLS) modeling and those of Hellinger distance (HD) metric to identify abnormal changes in highly correlated multivariate data. Specifically, the HD is used to quantify the dissimilarity between current NLPLS-based residual and reference probability distributions obtained using fault-free data. Furthermore, to enhance further the robustness of these methods to measurement noise, and reduce the false alarms due to modeling errors, wavelet-based multiscale filtering of residuals is used before the application of the HD-based monitoring scheme. The performances of the developed NLPLS-HD fault detection technique is illustrated using simulated plug flow reactor data. The results show that the proposed method provides favorable performance for detection of faults compared to the conventional NLPLS method.

  10. Model-based fault detection and isolation of a PWR nuclear power plant using neural networks

    International Nuclear Information System (INIS)

    Far, R.R.; Davilu, H.; Lucas, C.

    2008-01-01

    The proper and timely fault detection and isolation of industrial plant is of premier importance to guarantee the safe and reliable operation of industrial plants. The paper presents application of a neural networks-based scheme for fault detection and isolation, for the pressurizer of a PWR nuclear power plant. The scheme is constituted by 2 components: residual generation and fault isolation. The first component generates residuals via the discrepancy between measurements coming from the plant and a nominal model. The neutral network estimator is trained with healthy data collected from a full-scale simulator. For the second component detection thresholds are used to encode the residuals as bipolar vectors which represent fault patterns. These patterns are stored in an associative memory based on a recurrent neutral network. The proposed fault diagnosis tool is evaluated on-line via a full-scale simulator detected and isolate the main faults appearing in the pressurizer of a PWR. (orig.)

  11. SVD and Hankel matrix based de-noising approach for ball bearing fault detection and its assessment using artificial faults

    Science.gov (United States)

    Golafshan, Reza; Yuce Sanliturk, Kenan

    2016-03-01

    Ball bearings remain one of the most crucial components in industrial machines and due to their critical role, it is of great importance to monitor their conditions under operation. However, due to the background noise in acquired signals, it is not always possible to identify probable faults. This incapability in identifying the faults makes the de-noising process one of the most essential steps in the field of Condition Monitoring (CM) and fault detection. In the present study, Singular Value Decomposition (SVD) and Hankel matrix based de-noising process is successfully applied to the ball bearing time domain vibration signals as well as to their spectrums for the elimination of the background noise and the improvement the reliability of the fault detection process. The test cases conducted using experimental as well as the simulated vibration signals demonstrate the effectiveness of the proposed de-noising approach for the ball bearing fault detection.

  12. Statistical fault detection in photovoltaic systems

    KAUST Repository

    Garoudja, Elyes; Harrou, Fouzi; Sun, Ying; Kara, Kamel; Chouder, Aissa; Silvestre, Santiago

    2017-01-01

    and efficiency. Here, an innovative model-based fault-detection approach for early detection of shading of PV modules and faults on the direct current (DC) side of PV systems is proposed. This approach combines the flexibility, and simplicity of a one-diode model

  13. Induced Voltages Ratio-Based Algorithm for Fault Detection, and Faulted Phase and Winding Identification of a Three-Winding Power Transformer

    Directory of Open Access Journals (Sweden)

    Byung Eun Lee

    2014-09-01

    Full Text Available This paper proposes an algorithm for fault detection, faulted phase and winding identification of a three-winding power transformer based on the induced voltages in the electrical power system. The ratio of the induced voltages of the primary-secondary, primary-tertiary and secondary-tertiary windings is the same as the corresponding turns ratio during normal operating conditions, magnetic inrush, and over-excitation. It differs from the turns ratio during an internal fault. For a single phase and a three-phase power transformer with wye-connected windings, the induced voltages of each pair of windings are estimated. For a three-phase power transformer with delta-connected windings, the induced voltage differences are estimated to use the line currents, because the delta winding currents are practically unavailable. Six detectors are suggested for fault detection. An additional three detectors and a rule for faulted phase and winding identification are presented as well. The proposed algorithm can not only detect an internal fault, but also identify the faulted phase and winding of a three-winding power transformer. The various test results with Electromagnetic Transients Program (EMTP-generated data show that the proposed algorithm successfully discriminates internal faults from normal operating conditions including magnetic inrush and over-excitation. This paper concludes by implementing the algorithm into a prototype relay based on a digital signal processor.

  14. FUZZY FAULT DETECTION FOR PERMANENT MAGNET SYNCHRONOUS GENERATOR

    Directory of Open Access Journals (Sweden)

    N. Selvaganesan

    2011-07-01

    Full Text Available Faults in engineering systems are difficult to avoid and may result in serious consequences. Effective fault detection and diagnosis can improve system reliability and avoid expensive maintenance. In this paper fuzzy system based fault detection scheme for permanent magnet synchronous generator is proposed. The sequence current components like positive and negative sequence currents are used as fault indicators and given as inputs to fuzzy fault detector. Also, the fuzzy inference system is created and rule base is evaluated, relating the sequence current component to the type of faults. These rules are fired for specific changes in sequence current component and the faults are detected. The feasibility of the proposed scheme for permanent magnet synchronous generator is demonstrated for different types of fault under various operating conditions using MATLAB/Simulink.

  15. A measurement-based fault detection approach applied to monitor robots swarm

    KAUST Repository

    Khaldi, Belkacem

    2017-07-10

    Swarm robotics requires continuous monitoring to detect abnormal events and to sustain normal operations. Indeed, swarm robotics with one or more faulty robots leads to degradation of performances complying with the target requirements. This paper present an innovative data-driven fault detection method for monitoring robots swarm. The method combines the flexibility of principal component analysis (PCA) models and the greater sensitivity of the exponentially-weighted moving average control chart to incipient changes. We illustrate through simulated data collected from the ARGoS simulator that a significant improvement in fault detection can be obtained by using the proposed methods as compared to the use of the conventional PCA-based methods.

  16. Fault-tolerant control for current sensors of doubly fed induction generators based on an improved fault detection method

    DEFF Research Database (Denmark)

    Li, Hui; Yang, Chao; Hu, Yaogang

    2014-01-01

    Fault-tolerant control of current sensors is studied in this paper to improve the reliability of a doubly fed induction generator (DFIG). A fault-tolerant control system of current sensors is presented for the DFIG, which consists of a new current observer and an improved current sensor fault...... detection algorithm, and fault-tolerant control system are investigated by simulation. The results indicate that the outputs of the observer and the sensor are highly coherent. The fault detection algorithm can efficiently detect both soft and hard faults in current sensors, and the fault-tolerant control...

  17. Detection of Early Faults in Rotating Machinery Based on Wavelet Analysis

    Directory of Open Access Journals (Sweden)

    Meng Hee Lim

    2013-01-01

    Full Text Available This paper explores the application of wavelet analysis for the detection of early changes in rotor dynamics caused by common machinery faults, namely, rotor unbalance and minor blade rubbing conditions. In this paper, the time synchronised wavelet analysis method was formulated and its effectiveness to detect machinery faults at the early stage was evaluated based on signal simulation and experimental study. The proposed method provides a more standardised approach to visualise the current state of rotor dynamics of a rotating machinery by taking into account the effects of time shift, wavelet edge distortion, and system noise suppression. The experimental results showed that this method is able to reveal subtle changes of the vibration signal characteristics in both the frequency content distribution and the amplitude distortion caused by minor rotor unbalance and blade rubbing conditions. Besides, this method also appeared to be an effective tool to diagnose and to discriminate the different types of machinery faults based on the unique pattern of the wavelet contours. This study shows that the proposed wavelet analysis method is promising to reveal machinery faults at early stage as compared to vibration spectrum analysis.

  18. Parameter-free bearing fault detection based on maximum likelihood estimation and differentiation

    International Nuclear Information System (INIS)

    Bozchalooi, I Soltani; Liang, Ming

    2009-01-01

    Bearing faults can lead to malfunction and ultimately complete stall of many machines. The conventional high-frequency resonance (HFR) method has been commonly used for bearing fault detection. However, it is often very difficult to obtain and calibrate bandpass filter parameters, i.e. the center frequency and bandwidth, the key to the success of the HFR method. This inevitably undermines the usefulness of the conventional HFR technique. To avoid such difficulties, we propose parameter-free, versatile yet straightforward techniques to detect bearing faults. We focus on two types of measured signals frequently encountered in practice: (1) a mixture of impulsive faulty bearing vibrations and intrinsic background noise and (2) impulsive faulty bearing vibrations blended with intrinsic background noise and vibration interferences. To design a proper signal processing technique for each case, we analyze the effects of intrinsic background noise and vibration interferences on amplitude demodulation. For the first case, a maximum likelihood-based fault detection method is proposed to accommodate the Rician distribution of the amplitude-demodulated signal mixture. For the second case, we first illustrate that the high-amplitude low-frequency vibration interferences can make the amplitude demodulation ineffective. Then we propose a differentiation method to enhance the fault detectability. It is shown that the iterative application of a differentiation step can boost the relative strength of the impulsive faulty bearing signal component with respect to the vibration interferences. This preserves the effectiveness of amplitude demodulation and hence leads to more accurate fault detection. The proposed approaches are evaluated on simulated signals and experimental data acquired from faulty bearings

  19. Model-based fault detection for proton exchange membrane fuel cell ...

    African Journals Online (AJOL)

    In this paper, an intelligent model-based fault detection (FD) is developed for proton exchange membrane fuel cell (PEMFC) dynamic systems using an independent radial basis function (RBF) networks. The novelty is that this RBF networks is used to model the PEMFC dynamic systems and residuals are generated based ...

  20. Fault detection for hydraulic pump based on chaotic parallel RBF network

    Directory of Open Access Journals (Sweden)

    Ma Ning

    2011-01-01

    Full Text Available Abstract In this article, a parallel radial basis function network in conjunction with chaos theory (CPRBF network is presented, and applied to practical fault detection for hydraulic pump, which is a critical component in aircraft. The CPRBF network consists of a number of radial basis function (RBF subnets connected in parallel. The number of input nodes for each RBF subnet is determined by different embedding dimension based on chaotic phase-space reconstruction. The output of CPRBF is a weighted sum of all RBF subnets. It was first trained using the dataset from normal state without fault, and then a residual error generator was designed to detect failures based on the trained CPRBF network. Then, failure detection can be achieved by the analysis of the residual error. Finally, two case studies are introduced to compare the proposed CPRBF network with traditional RBF networks, in terms of prediction and detection accuracy.

  1. Hybrid Model-Based and Data-Driven Fault Detection and Diagnostics for Commercial Buildings: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Frank, Stephen; Heaney, Michael; Jin, Xin; Robertson, Joseph; Cheung, Howard; Elmore, Ryan; Henze, Gregor

    2016-08-01

    Commercial buildings often experience faults that produce undesirable behavior in building systems. Building faults waste energy, decrease occupants' comfort, and increase operating costs. Automated fault detection and diagnosis (FDD) tools for buildings help building owners discover and identify the root causes of faults in building systems, equipment, and controls. Proper implementation of FDD has the potential to simultaneously improve comfort, reduce energy use, and narrow the gap between actual and optimal building performance. However, conventional rule-based FDD requires expensive instrumentation and valuable engineering labor, which limit deployment opportunities. This paper presents a hybrid, automated FDD approach that combines building energy models and statistical learning tools to detect and diagnose faults noninvasively, using minimal sensors, with little customization. We compare and contrast the performance of several hybrid FDD algorithms for a small security building. Our results indicate that the algorithms can detect and diagnose several common faults, but more work is required to reduce false positive rates and improve diagnosis accuracy.

  2. Automated valve fault detection based on acoustic emission parameters and support vector machine

    Directory of Open Access Journals (Sweden)

    Salah M. Ali

    2018-03-01

    Full Text Available Reciprocating compressors are one of the most used types of compressors with wide applications in industry. The most common failure in reciprocating compressors is always related to the valves. Therefore, a reliable condition monitoring method is required to avoid the unplanned shutdown in this category of machines. Acoustic emission (AE technique is one of the effective recent methods in the field of valve condition monitoring. However, a major challenge is related to the analysis of AE signal which perhaps only depends on the experience and knowledge of technicians. This paper proposes automated fault detection method using support vector machine (SVM and AE parameters in an attempt to reduce human intervention in the process. Experiments were conducted on a single stage reciprocating air compressor by combining healthy and faulty valve conditions to acquire the AE signals. Valve functioning was identified through AE waveform analysis. SVM faults detection model was subsequently devised and validated based on training and testing samples respectively. The results demonstrated automatic valve fault detection model with accuracy exceeding 98%. It is believed that valve faults can be detected efficiently without human intervention by employing the proposed model for a single stage reciprocating compressor. Keywords: Condition monitoring, Faults detection, Signal analysis, Acoustic emission, Support vector machine

  3. FAULT DETECTION AND LOCALIZATION IN MOTORCYCLES BASED ON THE CHAIN CODE OF PSEUDOSPECTRA AND ACOUSTIC SIGNALS

    Directory of Open Access Journals (Sweden)

    B. S. Anami

    2013-06-01

    Full Text Available Vehicles produce sound signals with varying temporal and spectral properties under different working conditions. These sounds are indicative of the condition of the engine. Fault diagnosis is a significantly difficult task in geographically remote places where expertise is scarce. Automated fault diagnosis can assist riders to assess the health condition of their vehicles. This paper presents a method for fault detection and location in motorcycles based on the chain code of the pseudospectra and Mel-frequency cepstral coefficient (MFCC features of acoustic signals. The work comprises two stages: fault detection and fault location. The fault detection stage uses the chain code of the pseudospectrum as a feature vector. If the motorcycle is identified as faulty, the MFCCs of the same sample are computed and used as features for fault location. Both stages employ dynamic time warping for the classification of faults. Five types of faults in motorcycles are considered in this work. Observed classification rates are over 90% for the fault detection stage and over 94% for the fault location stage. The work identifies other interesting applications in the development of acoustic fingerprints for fault diagnosis of machinery, tuning of musical instruments, medical diagnosis, etc.

  4. Data Fault Detection in Medical Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yang Yang

    2015-03-01

    Full Text Available Medical body sensors can be implanted or attached to the human body to monitor the physiological parameters of patients all the time. Inaccurate data due to sensor faults or incorrect placement on the body will seriously influence clinicians’ diagnosis, therefore detecting sensor data faults has been widely researched in recent years. Most of the typical approaches to sensor fault detection in the medical area ignore the fact that the physiological indexes of patients aren’t changing synchronously at the same time, and fault values mixed with abnormal physiological data due to illness make it difficult to determine true faults. Based on these facts, we propose a Data Fault Detection mechanism in Medical sensor networks (DFD-M. Its mechanism includes: (1 use of a dynamic-local outlier factor (D-LOF algorithm to identify outlying sensed data vectors; (2 use of a linear regression model based on trapezoidal fuzzy numbers to predict which readings in the outlying data vector are suspected to be faulty; (3 the proposal of a novel judgment criterion of fault state according to the prediction values. The simulation results demonstrate the efficiency and superiority of DFD-M.

  5. A new fault detection method for computer networks

    International Nuclear Information System (INIS)

    Lu, Lu; Xu, Zhengguo; Wang, Wenhai; Sun, Youxian

    2013-01-01

    Over the past few years, fault detection for computer networks has attracted extensive attentions for its importance in network management. Most existing fault detection methods are based on active probing techniques which can detect the occurrence of faults fast and precisely. But these methods suffer from the limitation of traffic overhead, especially in large scale networks. To relieve traffic overhead induced by active probing based methods, a new fault detection method, whose key is to divide the detection process into multiple stages, is proposed in this paper. During each stage, only a small region of the network is detected by using a small set of probes. Meanwhile, it also ensures that the entire network can be covered after multiple detection stages. This method can guarantee that the traffic used by probes during each detection stage is small sufficiently so that the network can operate without severe disturbance from probes. Several simulation results verify the effectiveness of the proposed method

  6. Data-driven fault detection for industrial processes canonical correlation analysis and projection based methods

    CERN Document Server

    Chen, Zhiwen

    2017-01-01

    Zhiwen Chen aims to develop advanced fault detection (FD) methods for the monitoring of industrial processes. With the ever increasing demands on reliability and safety in industrial processes, fault detection has become an important issue. Although the model-based fault detection theory has been well studied in the past decades, its applications are limited to large-scale industrial processes because it is difficult to build accurate models. Furthermore, motivated by the limitations of existing data-driven FD methods, novel canonical correlation analysis (CCA) and projection-based methods are proposed from the perspectives of process input and output data, less engineering effort and wide application scope. For performance evaluation of FD methods, a new index is also developed. Contents A New Index for Performance Evaluation of FD Methods CCA-based FD Method for the Monitoring of Stationary Processes Projection-based FD Method for the Monitoring of Dynamic Processes Benchmark Study and Real-Time Implementat...

  7. Statistical fault detection in photovoltaic systems

    KAUST Repository

    Garoudja, Elyes

    2017-05-08

    Faults in photovoltaic (PV) systems, which can result in energy loss, system shutdown or even serious safety breaches, are often difficult to avoid. Fault detection in such systems is imperative to improve their reliability, productivity, safety and efficiency. Here, an innovative model-based fault-detection approach for early detection of shading of PV modules and faults on the direct current (DC) side of PV systems is proposed. This approach combines the flexibility, and simplicity of a one-diode model with the extended capacity of an exponentially weighted moving average (EWMA) control chart to detect incipient changes in a PV system. The one-diode model, which is easily calibrated due to its limited calibration parameters, is used to predict the healthy PV array\\'s maximum power coordinates of current, voltage and power using measured temperatures and irradiances. Residuals, which capture the difference between the measurements and the predictions of the one-diode model, are generated and used as fault indicators. Then, the EWMA monitoring chart is applied on the uncorrelated residuals obtained from the one-diode model to detect and identify the type of fault. Actual data from the grid-connected PV system installed at the Renewable Energy Development Center, Algeria, are used to assess the performance of the proposed approach. Results show that the proposed approach successfully monitors the DC side of PV systems and detects temporary shading.

  8. Linear discriminant analysis for welding fault detection

    International Nuclear Information System (INIS)

    Li, X.; Simpson, S.W.

    2010-01-01

    This work presents a new method for real time welding fault detection in industry based on Linear Discriminant Analysis (LDA). A set of parameters was calculated from one second blocks of electrical data recorded during welding and based on control data from reference welds under good conditions, as well as faulty welds. Optimised linear combinations of the parameters were determined with LDA and tested with independent data. Short arc welds in overlap joints were studied with various power sources, shielding gases, wire diameters, and process geometries. Out-of-position faults were investigated. Application of LDA fault detection to a broad range of welding procedures was investigated using a similarity measure based on Principal Component Analysis. The measure determines which reference data are most similar to a given industrial procedure and the appropriate LDA weights are then employed. Overall, results show that Linear Discriminant Analysis gives an effective and consistent performance in real-time welding fault detection.

  9. Fault Detection and Diagnosis System in Process industry Based on Big Data and WeChat

    Directory of Open Access Journals (Sweden)

    Sun Zengqiang

    2017-01-01

    Full Text Available The fault detection and diagnosis information in process industry can be received, anytime and anywhere, based on bigdata and WeChat with mobile phone, which got rid of constraints that can only check Distributed Control System (DCS in the central control room or look over in office. Then, fault detection, diagnosis information sharing can be provided, and what’s more, fault detection alarm range, code and inform time can be personalized. The pressure of managers who worked on process industry can be release with the mobile information system.

  10. Fault Detection Based on Tracking Differentiator Applied on the Suspension System of Maglev Train

    Directory of Open Access Journals (Sweden)

    Hehong Zhang

    2015-01-01

    Full Text Available A fault detection method based on the optimized tracking differentiator is introduced. It is applied on the acceleration sensor of the suspension system of maglev train. It detects the fault of the acceleration sensor by comparing the acceleration integral signal with the speed signal obtained by the optimized tracking differentiator. This paper optimizes the control variable when the states locate within or beyond the two-step reachable region to improve the performance of the approximate linear discrete tracking differentiator. Fault-tolerant control has been conducted by feedback based on the speed signal acquired from the optimized tracking differentiator when the acceleration sensor fails. The simulation and experiment results show the practical usefulness of the presented method.

  11. Latest Progress of Fault Detection and Localization in Complex Electrical Engineering

    Science.gov (United States)

    Zhao, Zheng; Wang, Can; Zhang, Yagang; Sun, Yi

    2014-01-01

    In the researches of complex electrical engineering, efficient fault detection and localization schemes are essential to quickly detect and locate faults so that appropriate and timely corrective mitigating and maintenance actions can be taken. In this paper, under the current measurement precision of PMU, we will put forward a new type of fault detection and localization technology based on fault factor feature extraction. Lots of simulating experiments indicate that, although there are disturbances of white Gaussian stochastic noise, based on fault factor feature extraction principal, the fault detection and localization results are still accurate and reliable, which also identifies that the fault detection and localization technology has strong anti-interference ability and great redundancy.

  12. Fault Detection and Isolation using Eigenstructure Assignment

    DEFF Research Database (Denmark)

    Jørgensen, R. B.; Patton, R.; Chen, J.

    1994-01-01

    The purpose of this article is to investigate the robustness to model uncertainties of observer based fault detection and isolation. The approach is designed with a straight forward dynamic nad the observer.......The purpose of this article is to investigate the robustness to model uncertainties of observer based fault detection and isolation. The approach is designed with a straight forward dynamic nad the observer....

  13. A Method Based on Multi-Sensor Data Fusion for Fault Detection of Planetary Gearboxes

    Directory of Open Access Journals (Sweden)

    Detong Kong

    2012-02-01

    Full Text Available Studies on fault detection and diagnosis of planetary gearboxes are quite limited compared with those of fixed-axis gearboxes. Different from fixed-axis gearboxes, planetary gearboxes exhibit unique behaviors, which invalidate fault diagnosis methods that work well for fixed-axis gearboxes. It is a fact that for systems as complex as planetary gearboxes, multiple sensors mounted on different locations provide complementary information on the health condition of the systems. On this basis, a fault detection method based on multi-sensor data fusion is introduced in this paper. In this method, two features developed for planetary gearboxes are used to characterize the gear health conditions, and an adaptive neuro-fuzzy inference system (ANFIS is utilized to fuse all features from different sensors. In order to demonstrate the effectiveness of the proposed method, experiments are carried out on a planetary gearbox test rig, on which multiple accelerometers are mounted for data collection. The comparisons between the proposed method and the methods based on individual sensors show that the former achieves much higher accuracies in detecting planetary gearbox faults.

  14. Fault Detection based on MCSA for a 400Hz Asynchronous Motor for Airborne Applications

    Directory of Open Access Journals (Sweden)

    Steffen Haus

    2013-01-01

    Full Text Available Future health monitoring concepts in different fields of engineering require reliable fault detection to avoid unscheduled machine downtime. Diagnosis of electrical induction machines for industrial applications is widely discussed in literature. In aviation industry, this topic is still only rarely discussed.A common approach to health monitoring for electrical induction machines is to use Motor Current Signature Analysis (MCSA based on a Fast Fourier Transform (FFT. Research results on this topic are available for comparatively large motors, where the power supply is typically based on 50Hz alternating current, which is the general power supply frequency for industrial applications.In this paper, transferability to airborne applications, where the power supply is 400Hz, is assessed. Three phase asynchronous motors are used to analyse detectability of different motor faults. The possibility to transfer fault detection results from 50Hz to 400Hz induction machines is the main question answered in this research work. 400Hz power supply frequency requires adjusted motor design, causing increased motor speed compared to 50Hz supply frequency. The motor used for experiments in this work is a 800W motor with 200V phase to phase power supply, powering an avionic fan. The fault cases to be examined are a bearing fault, a rotor unbalance, a stator winding fault, a broken rotor bar and a static air gap eccentricity. These are the most common faults in electrical induction machines which can cause machine downtime. The focus of the research work is the feasibility of the application of MCSA for small scale, high speed motor design, using the Fourier spectra of the current signal.Detectability is given for all but the bearing fault, although rotor unbalance can only be detected in case of severe damage level. Results obtained in the experiments are interpreted with respect to the motor design. Physical interpretation are given in case the results differ

  15. Performance based fault diagnosis

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik

    2002-01-01

    Different aspects of fault detection and fault isolation in closed-loop systems are considered. It is shown that using the standard setup known from feedback control, it is possible to formulate fault diagnosis problems based on a performance index in this general standard setup. It is also shown...

  16. Fault Detection for Nonlinear Process With Deterministic Disturbances: A Just-In-Time Learning Based Data Driven Method.

    Science.gov (United States)

    Yin, Shen; Gao, Huijun; Qiu, Jianbin; Kaynak, Okyay

    2017-11-01

    Data-driven fault detection plays an important role in industrial systems due to its applicability in case of unknown physical models. In fault detection, disturbances must be taken into account as an inherent characteristic of processes. Nevertheless, fault detection for nonlinear processes with deterministic disturbances still receive little attention, especially in data-driven field. To solve this problem, a just-in-time learning-based data-driven (JITL-DD) fault detection method for nonlinear processes with deterministic disturbances is proposed in this paper. JITL-DD employs JITL scheme for process description with local model structures to cope with processes dynamics and nonlinearity. The proposed method provides a data-driven fault detection solution for nonlinear processes with deterministic disturbances, and owns inherent online adaptation and high accuracy of fault detection. Two nonlinear systems, i.e., a numerical example and a sewage treatment process benchmark, are employed to show the effectiveness of the proposed method.

  17. PV Systems Reliability Final Technical Report: Ground Fault Detection

    Energy Technology Data Exchange (ETDEWEB)

    Lavrova, Olga [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Flicker, Jack David [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Johnson, Jay [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2016-01-01

    We have examined ground faults in PhotoVoltaic (PV) arrays and the efficacy of fuse, current detection (RCD), current sense monitoring/relays (CSM), isolation/insulation (Riso) monitoring, and Ground Fault Detection and Isolation (GFID) using simulations based on a Simulation Program with Integrated Circuit Emphasis SPICE ground fault circuit model, experimental ground faults installed on real arrays, and theoretical equations.

  18. A Model-Based Probabilistic Inversion Framework for Wire Fault Detection Using TDR

    Science.gov (United States)

    Schuet, Stefan R.; Timucin, Dogan A.; Wheeler, Kevin R.

    2010-01-01

    Time-domain reflectometry (TDR) is one of the standard methods for diagnosing faults in electrical wiring and interconnect systems, with a long-standing history focused mainly on hardware development of both high-fidelity systems for laboratory use and portable hand-held devices for field deployment. While these devices can easily assess distance to hard faults such as sustained opens or shorts, their ability to assess subtle but important degradation such as chafing remains an open question. This paper presents a unified framework for TDR-based chafing fault detection in lossy coaxial cables by combining an S-parameter based forward modeling approach with a probabilistic (Bayesian) inference algorithm. Results are presented for the estimation of nominal and faulty cable parameters from laboratory data.

  19. Solar system fault detection

    Science.gov (United States)

    Farrington, R.B.; Pruett, J.C. Jr.

    1984-05-14

    A fault detecting apparatus and method are provided for use with an active solar system. The apparatus provides an indication as to whether one or more predetermined faults have occurred in the solar system. The apparatus includes a plurality of sensors, each sensor being used in determining whether a predetermined condition is present. The outputs of the sensors are combined in a pre-established manner in accordance with the kind of predetermined faults to be detected. Indicators communicate with the outputs generated by combining the sensor outputs to give the user of the solar system and the apparatus an indication as to whether a predetermined fault has occurred. Upon detection and indication of any predetermined fault, the user can take appropriate corrective action so that the overall reliability and efficiency of the active solar system are increased.

  20. An effort allocation model considering different budgetary constraint on fault detection process and fault correction process

    Directory of Open Access Journals (Sweden)

    Vijay Kumar

    2016-01-01

    Full Text Available Fault detection process (FDP and Fault correction process (FCP are important phases of software development life cycle (SDLC. It is essential for software to undergo a testing phase, during which faults are detected and corrected. The main goal of this article is to allocate the testing resources in an optimal manner to minimize the cost during testing phase using FDP and FCP under dynamic environment. In this paper, we first assume there is a time lag between fault detection and fault correction. Thus, removal of a fault is performed after a fault is detected. In addition, detection process and correction process are taken to be independent simultaneous activities with different budgetary constraints. A structured optimal policy based on optimal control theory is proposed for software managers to optimize the allocation of the limited resources with the reliability criteria. Furthermore, release policy for the proposed model is also discussed. Numerical example is given in support of the theoretical results.

  1. Fault Diagnosis for Actuators in a Class of Nonlinear Systems Based on an Adaptive Fault Detection Observer

    Directory of Open Access Journals (Sweden)

    Runxia Guo

    2016-01-01

    Full Text Available The problem of actuators’ fault diagnosis is pursued for a class of nonlinear control systems that are affected by bounded measurement noise and external disturbances. A novel fault diagnosis algorithm has been proposed by combining the idea of adaptive control theory and the approach of fault detection observer. The asymptotical stability of the fault detection observer is guaranteed by setting the adaptive adjusting law of the unknown fault vector. A theoretically rigorous proof of asymptotical stability has been given. Under the condition that random measurement noise generated by the sensors of control systems and external disturbances exist simultaneously, the designed fault diagnosis algorithm is able to successfully give specific estimated values of state variables and failures rather than just giving a simple fault warning. Moreover, the proposed algorithm is very simple and concise and is easy to be applied to practical engineering. Numerical experiments are carried out to evaluate the performance of the fault diagnosis algorithm. Experimental results show that the proposed diagnostic strategy has a satisfactory estimation effect.

  2. Observer-Based and Regression Model-Based Detection of Emerging Faults in Coal Mills

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Lin, Bao; Jørgensen, Sten Bay

    2006-01-01

    In order to improve the reliability of power plants it is important to detect fault as fast as possible. Doing this it is interesting to find the most efficient method. Since modeling of large scale systems is time consuming it is interesting to compare a model-based method with data driven ones....

  3. Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection

    DEFF Research Database (Denmark)

    Schlechtingen, Meik; Santos, Ilmar

    2011-01-01

    This paper presents the research results of a comparison of three different model based approaches for wind turbine fault detection in online SCADA data, by applying developed models to five real measured faults and anomalies. The regression based model as the simplest approach to build a normal...

  4. Wind turbine fault detection and fault tolerant control

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Johnson, Kathryn

    2013-01-01

    In this updated edition of a previous wind turbine fault detection and fault tolerant control challenge, we present a more sophisticated wind turbine model and updated fault scenarios to enhance the realism of the challenge and therefore the value of the solutions. This paper describes...

  5. Fault detection in finite frequency domain for Takagi-Sugeno fuzzy systems with sensor faults.

    Science.gov (United States)

    Li, Xiao-Jian; Yang, Guang-Hong

    2014-08-01

    This paper is concerned with the fault detection (FD) problem in finite frequency domain for continuous-time Takagi-Sugeno fuzzy systems with sensor faults. Some finite-frequency performance indices are initially introduced to measure the fault/reference input sensitivity and disturbance robustness. Based on these performance indices, an effective FD scheme is then presented such that the generated residual is designed to be sensitive to both fault and reference input for faulty cases, while robust against the reference input for fault-free case. As the additional reference input sensitivity for faulty cases is considered, it is shown that the proposed method improves the existing FD techniques and achieves a better FD performance. The theory is supported by simulation results related to the detection of sensor faults in a tunnel-diode circuit.

  6. Voltage Based Detection Method for High Impedance Fault in a Distribution System

    Science.gov (United States)

    Thomas, Mini Shaji; Bhaskar, Namrata; Prakash, Anupama

    2016-09-01

    High-impedance faults (HIFs) on distribution feeders cannot be detected by conventional protection schemes, as HIFs are characterized by their low fault current level and waveform distortion due to the nonlinearity of the ground return path. This paper proposes a method to identify the HIFs in distribution system and isolate the faulty section, to reduce downtime. This method is based on voltage measurements along the distribution feeder and utilizes the sequence components of the voltages. Three models of high impedance faults have been considered and source side and load side breaking of the conductor have been studied in this work to capture a wide range of scenarios. The effect of neutral grounding of the source side transformer is also accounted in this study. The results show that the algorithm detects the HIFs accurately and rapidly. Thus, the faulty section can be isolated and service can be restored to the rest of the consumers.

  7. Detection of Partial Demagnetization Fault in PMSMs Operating under Nonstationary Conditions

    DEFF Research Database (Denmark)

    Wang, Chao; Delgado Prieto, Miguel; Romeral, Luis

    2016-01-01

    Demagnetization fault detection of in-service Permanent Magnet Synchronous Machines (PMSMs) is a challenging task because most PMSMs operate under nonstationary circumstances in industrial applications. A novel approach based on tracking characteristic orders of stator current using Vold-Kalman F......Demagnetization fault detection of in-service Permanent Magnet Synchronous Machines (PMSMs) is a challenging task because most PMSMs operate under nonstationary circumstances in industrial applications. A novel approach based on tracking characteristic orders of stator current using Vold......-Kalman Filter is proposed to detect the partial demagnetization fault in PMSMs running at nonstationary conditions. Amplitude of envelope of the fault characteristic orders is used as fault indictor. Experimental results verify the superiority of the proposed method on partial demagnetization online fault...... detection of PMSMs under various speed and load conditions....

  8. FaultBuster: data driven fault detection and diagnosis for industrial systems

    DEFF Research Database (Denmark)

    Bergantino, Nicola; Caponetti, Fabio; Longhi, Sauro

    2009-01-01

    . Multivariate statistical models based on principal components are used to detect abnormal situations. Tailored to alarms, a probabilistic inference engine process the fault evidences to output the most probable diagnosis. Results from the DX 09 Diagnostic Challenge shown strong detection properties, while...

  9. The Fault Detection, Localization, and Tolerant Operation of Modular Multilevel Converters with an Insulated Gate Bipolar Transistor (IGBT Open Circuit Fault

    Directory of Open Access Journals (Sweden)

    Wei Li

    2018-04-01

    Full Text Available Reliability is one of the critical issues for a modular multilevel converter (MMC since it consists of a large number of series-connected power electronics submodules (SMs. In this paper, a complete control strategy including fault detection, localization, and tolerant operation is proposed for the MMC under an insulated gate bipolar transistor (IGBT open circuit fault. According to the output characteristics of the SM with the open-circuit fault of IGBT, a fault detection method based on the circulating current and output current observation is used. In order to further precisely locate the position of the faulty SM, a fault localization method based on the SM capacitor voltage observation is developed. After the faulty SM is isolated, the continuous operation of the converter is ensured by adopting the fault-tolerant strategy based on the use of redundant modules. To verify the proposed fault detection, fault localization, and fault-tolerant operation strategies, a 900 kVA MMC system under the conditions of an IGBT open circuit is developed in the Matlab/Simulink platform. The capabilities of rapid detection, precise positioning, and fault-tolerant operation of the investigated detection and control algorithms are also demonstrated.

  10. A Novel Online Data-Driven Algorithm for Detecting UAV Navigation Sensor Faults.

    Science.gov (United States)

    Sun, Rui; Cheng, Qi; Wang, Guanyu; Ochieng, Washington Yotto

    2017-09-29

    The use of Unmanned Aerial Vehicles (UAVs) has increased significantly in recent years. On-board integrated navigation sensors are a key component of UAVs' flight control systems and are essential for flight safety. In order to ensure flight safety, timely and effective navigation sensor fault detection capability is required. In this paper, a novel data-driven Adaptive Neuron Fuzzy Inference System (ANFIS)-based approach is presented for the detection of on-board navigation sensor faults in UAVs. Contrary to the classic UAV sensor fault detection algorithms, based on predefined or modelled faults, the proposed algorithm combines an online data training mechanism with the ANFIS-based decision system. The main advantages of this algorithm are that it allows real-time model-free residual analysis from Kalman Filter (KF) estimates and the ANFIS to build a reliable fault detection system. In addition, it allows fast and accurate detection of faults, which makes it suitable for real-time applications. Experimental results have demonstrated the effectiveness of the proposed fault detection method in terms of accuracy and misdetection rate.

  11. A Novel Online Data-Driven Algorithm for Detecting UAV Navigation Sensor Faults

    Directory of Open Access Journals (Sweden)

    Rui Sun

    2017-09-01

    Full Text Available The use of Unmanned Aerial Vehicles (UAVs has increased significantly in recent years. On-board integrated navigation sensors are a key component of UAVs’ flight control systems and are essential for flight safety. In order to ensure flight safety, timely and effective navigation sensor fault detection capability is required. In this paper, a novel data-driven Adaptive Neuron Fuzzy Inference System (ANFIS-based approach is presented for the detection of on-board navigation sensor faults in UAVs. Contrary to the classic UAV sensor fault detection algorithms, based on predefined or modelled faults, the proposed algorithm combines an online data training mechanism with the ANFIS-based decision system. The main advantages of this algorithm are that it allows real-time model-free residual analysis from Kalman Filter (KF estimates and the ANFIS to build a reliable fault detection system. In addition, it allows fast and accurate detection of faults, which makes it suitable for real-time applications. Experimental results have demonstrated the effectiveness of the proposed fault detection method in terms of accuracy and misdetection rate.

  12. Customized Multiwavelets for Planetary Gearbox Fault Detection Based on Vibration Sensor Signals

    Directory of Open Access Journals (Sweden)

    Lue Chen

    2013-01-01

    Full Text Available Planetary gearboxes exhibit complicated dynamic responses which are more difficult to detect in vibration signals than fixed-axis gear trains because of the special gear transmission structures. Diverse advanced methods have been developed for this challenging task to reduce or avoid unscheduled breakdown and catastrophic accidents. It is feasible to make fault features distinct by using multiwavelet denoising which depends on the feature separation and the threshold denoising. However, standard and fixed multiwavelets are not suitable for accurate fault feature detections because they are usually independent of the measured signals. To overcome this drawback, a method to construct customized multiwavelets based on the redundant symmetric lifting scheme is proposed in this paper. A novel indicator which combines kurtosis and entropy is applied to select the optimal multiwavelets, because kurtosis is sensitive to sharp impulses and entropy is effective for periodic impulses. The improved neighboring coefficients method is introduced into multiwavelet denoising. The vibration signals of a planetary gearbox from a satellite communication antenna on a measurement ship are captured under various motor speeds. The results show the proposed method could accurately detect the incipient pitting faults on two neighboring teeth in the planetary gearbox.

  13. Final Technical Report: PV Fault Detection Tool.

    Energy Technology Data Exchange (ETDEWEB)

    King, Bruce Hardison [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Jones, Christian Birk [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2015-12-01

    The PV Fault Detection Tool project plans to demonstrate that the FDT can (a) detect catastrophic and degradation faults and (b) identify the type of fault. This will be accomplished by collecting fault signatures using different instruments and integrating this information to establish a logical controller for detecting, diagnosing and classifying each fault.

  14. Metric Learning Method Aided Data-Driven Design of Fault Detection Systems

    Directory of Open Access Journals (Sweden)

    Guoyang Yan

    2014-01-01

    Full Text Available Fault detection is fundamental to many industrial applications. With the development of system complexity, the number of sensors is increasing, which makes traditional fault detection methods lose efficiency. Metric learning is an efficient way to build the relationship between feature vectors with the categories of instances. In this paper, we firstly propose a metric learning-based fault detection framework in fault detection. Meanwhile, a novel feature extraction method based on wavelet transform is used to obtain the feature vector from detection signals. Experiments on Tennessee Eastman (TE chemical process datasets demonstrate that the proposed method has a better performance when comparing with existing methods, for example, principal component analysis (PCA and fisher discriminate analysis (FDA.

  15. Norm based Threshold Selection for Fault Detectors

    DEFF Research Database (Denmark)

    Rank, Mike Lind; Niemann, Henrik

    1998-01-01

    The design of fault detectors for fault detection and isolation (FDI) in dynamic systems is considered from a norm based point of view. An analysis of norm based threshold selection is given based on different formulations of FDI problems. Both the nominal FDI problem as well as the uncertain FDI...... problem are considered. Based on this analysis, a performance index based on norms of the involved transfer functions is given. The performance index allows us also to optimize the structure of the fault detection filter directly...

  16. Robust fault detection of turbofan engines subject to adaptive controllers via a Total Measurable Fault Information Residual (ToMFIR) technique.

    Science.gov (United States)

    Chen, Wen; Chowdhury, Fahmida N; Djuric, Ana; Yeh, Chih-Ping

    2014-09-01

    This paper provides a new design of robust fault detection for turbofan engines with adaptive controllers. The critical issue is that the adaptive controllers can depress the faulty effects such that the actual system outputs remain the pre-specified values, making it difficult to detect faults/failures. To solve this problem, a Total Measurable Fault Information Residual (ToMFIR) technique with the aid of system transformation is adopted to detect faults in turbofan engines with adaptive controllers. This design is a ToMFIR-redundancy-based robust fault detection. The ToMFIR is first introduced and existing results are also summarized. The Detailed design process of the ToMFIRs is presented and a turbofan engine model is simulated to verify the effectiveness of the proposed ToMFIR-based fault-detection strategy. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

  17. Fault Detection and Isolation for Wind Turbine Electric Pitch System

    DEFF Research Database (Denmark)

    Zhu, Jiangsheng; Ma, Kuichao; Hajizadeh, Amin

    2017-01-01

    This paper presents a model-based fault detection and isolation scheme applied on electric pitch system of wind turbines. Pitch system is one of the most critical components due to its effect on the operational safety and the dynamics of wind turbines. Faults in this system should be precisely...... detected to prevent failures and decrease downtime. To detect faults of electric pitch actuators and sensors, an extended kalman filter (EKF) based multiple model adaptive estimation (MMAE) designed to estimate the states of the system. The proposed method is demonstrated in case studies. The simulation...

  18. A Game Theoretic Fault Detection Filter

    Science.gov (United States)

    Chung, Walter H.; Speyer, Jason L.

    1995-01-01

    The fault detection process is modelled as a disturbance attenuation problem. The solution to this problem is found via differential game theory, leading to an H(sub infinity) filter which bounds the transmission of all exogenous signals save the fault to be detected. For a general class of linear systems which includes some time-varying systems, it is shown that this transmission bound can be taken to zero by simultaneously bringing the sensor noise weighting to zero. Thus, in the limit, a complete transmission block can he achieved, making the game filter into a fault detection filter. When we specialize this result to time-invariant system, it is found that the detection filter attained in the limit is identical to the well known Beard-Jones Fault Detection Filter. That is, all fault inputs other than the one to be detected (the "nuisance faults") are restricted to an invariant subspace which is unobservable to a projection on the output. For time-invariant systems, it is also shown that in the limit, the order of the state-space and the game filter can be reduced by factoring out the invariant subspace. The result is a lower dimensional filter which can observe only the fault to be detected. A reduced-order filter can also he generated for time-varying systems, though the computational overhead may be intensive. An example given at the end of the paper demonstrates the effectiveness of the filter as a tool for fault detection and identification.

  19. Rapid detection of small oscillation faults via deterministic learning.

    Science.gov (United States)

    Wang, Cong; Chen, Tianrui

    2011-08-01

    Detection of small faults is one of the most important and challenging tasks in the area of fault diagnosis. In this paper, we present an approach for the rapid detection of small oscillation faults based on a recently proposed deterministic learning (DL) theory. The approach consists of two phases: the training phase and the test phase. In the training phase, the system dynamics underlying normal and fault oscillations are locally accurately approximated through DL. The obtained knowledge of system dynamics is stored in constant radial basis function (RBF) networks. In the diagnosis phase, rapid detection is implemented. Specially, a bank of estimators are constructed using the constant RBF neural networks to represent the training normal and fault modes. By comparing the set of estimators with the test monitored system, a set of residuals are generated, and the average L(1) norms of the residuals are taken as the measure of the differences between the dynamics of the monitored system and the dynamics of the training normal mode and oscillation faults. The occurrence of a test oscillation fault can be rapidly detected according to the smallest residual principle. A rigorous analysis of the performance of the detection scheme is also given. The novelty of the paper lies in that the modeling uncertainty and nonlinear fault functions are accurately approximated and then the knowledge is utilized to achieve rapid detection of small oscillation faults. Simulation studies are included to demonstrate the effectiveness of the approach.

  20. Fault Analysis and Detection in Microgrids with High PV Penetration

    Energy Technology Data Exchange (ETDEWEB)

    El Khatib, Mohamed [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Hernandez Alvidrez, Javier [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Ellis, Abraham [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-05-01

    In this report we focus on analyzing current-controlled PV inverters behaviour under faults in order to develop fault detection schemes for microgrids with high PV penetration. Inverter model suitable for steady state fault studies is presented and the impact of PV inverters on two protection elements is analyzed. The studied protection elements are superimposed quantities based directional element and negative sequence directional element. Additionally, several non-overcurrent fault detection schemes are discussed in this report for microgrids with high PV penetration. A detailed time-domain simulation study is presented to assess the performance of the presented fault detection schemes under different microgrid modes of operation.

  1. Internal Leakage Fault Detection and Tolerant Control of Single-Rod Hydraulic Actuators

    Directory of Open Access Journals (Sweden)

    Jianyong Yao

    2014-01-01

    Full Text Available The integration of internal leakage fault detection and tolerant control for single-rod hydraulic actuators is present in this paper. Fault detection is a potential technique to provide efficient condition monitoring and/or preventive maintenance, and fault tolerant control is a critical method to improve the safety and reliability of hydraulic servo systems. Based on quadratic Lyapunov functions, a performance-oriented fault detection method is proposed, which has a simple structure and is prone to implement in practice. The main feature is that, when a prescribed performance index is satisfied (even a slight fault has occurred, there is no fault alarmed; otherwise (i.e., a severe fault has occurred, the fault is detected and then a fault tolerant controller is activated. The proposed tolerant controller, which is based on the parameter adaptive methodology, is also prone to realize, and the learning mechanism is simple since only the internal leakage is considered in parameter adaptation and thus the persistent exciting (PE condition is easily satisfied. After the activation of the fault tolerant controller, the control performance is gradually recovered. Simulation results on a hydraulic servo system with both abrupt and incipient internal leakage fault demonstrate the effectiveness of the proposed fault detection and tolerant control method.

  2. Exact, almost and delayed fault detection

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Saberi, Ali; Stoorvogel, Anton A.

    1999-01-01

    Considers the problem of fault detection and isolation while using zero or almost zero threshold. A number of different fault detection and isolation problems using exact or almost exact disturbance decoupling are formulated. Solvability conditions are given for the formulated design problems....... The l-step delayed fault detection problem is also considered for discrete-time systems....

  3. Support vector machine based fault detection approach for RFT-30 cyclotron

    Energy Technology Data Exchange (ETDEWEB)

    Kong, Young Bae, E-mail: ybkong@kaeri.re.kr; Lee, Eun Je; Hur, Min Goo; Park, Jeong Hoon; Park, Yong Dae; Yang, Seung Dae

    2016-10-21

    An RFT-30 is a 30 MeV cyclotron used for radioisotope applications and radiopharmaceutical researches. The RFT-30 cyclotron is highly complex and includes many signals for control and monitoring of the system. It is quite difficult to detect and monitor the system failure in real time. Moreover, continuous monitoring of the system is hard and time-consuming work for human operators. In this paper, we propose a support vector machine (SVM) based fault detection approach for the RFT-30 cyclotron. The proposed approach performs SVM learning with training samples to construct the classification model. To compensate the system complexity due to the large-scale accelerator, we utilize the principal component analysis (PCA) for transformation of the original data. After training procedure, the proposed approach detects the system faults in real time. We analyzed the performance of the proposed approach utilizing the experimental data of the RFT-30 cyclotron. The performance results show that the proposed SVM approach can provide an efficient way to control the cyclotron system.

  4. Adaptive Fault Detection for Complex Dynamic Processes Based on JIT Updated Data Set

    Directory of Open Access Journals (Sweden)

    Jinna Li

    2012-01-01

    Full Text Available A novel fault detection technique is proposed to explicitly account for the nonlinear, dynamic, and multimodal problems existed in the practical and complex dynamic processes. Just-in-time (JIT detection method and k-nearest neighbor (KNN rule-based statistical process control (SPC approach are integrated to construct a flexible and adaptive detection scheme for the control process with nonlinear, dynamic, and multimodal cases. Mahalanobis distance, representing the correlation among samples, is used to simplify and update the raw data set, which is the first merit in this paper. Based on it, the control limit is computed in terms of both KNN rule and SPC method, such that we can identify whether the current data is normal or not by online approach. Noted that the control limit obtained changes with updating database such that an adaptive fault detection technique that can effectively eliminate the impact of data drift and shift on the performance of detection process is obtained, which is the second merit in this paper. The efficiency of the developed method is demonstrated by the numerical examples and an industrial case.

  5. Early fault detection and diagnosis for nuclear power plants

    International Nuclear Information System (INIS)

    Berg, O.; Grini, R.; Masao Yokobayashi

    1988-01-01

    Fault detection based on a number of reference models is demonstrated. This approach is characterized by the possibility of detecting faults before a traditional alarm system is triggered, even in dynamic situations. Further, by a proper decomposition scheme and use of available process measurements, the problem area can be confined to the faulty process parts. A diagnosis system using knowledge engineering techniques is described. Typical faults are classified and described by rules involving alarm patterns and variations of important parameters. By structuring the fault hypotheses in a hierarchy the search space is limited which is important for real time diagnosis. Introduction of certainty factors improve the flexibility and robustness of diagnosis by exploring parallel problems even when some data are missing. A new display proposal should facilitate the operator interface and the integration of fault detection and diagnosis tasks in disturbance handling. The techniques of early fault detection and diagnosis are presently being implemented and tested in the experimental control room of a full-scope PWR simulator in Halden

  6. Detection and Identification of Loss of Efficiency Faults of Flight Actuators

    Directory of Open Access Journals (Sweden)

    Ossmann Daniel

    2015-03-01

    Full Text Available We propose linear parameter-varying (LPV model-based approaches to the synthesis of robust fault detection and diagnosis (FDD systems for loss of efficiency (LOE faults of flight actuators. The proposed methods are applicable to several types of parametric (or multiplicative LOE faults such as actuator disconnection, surface damage, actuator power loss or stall loads. For the detection of these parametric faults, advanced LPV-model detection techniques are proposed, which implicitly provide fault identification information. Fast detection of intermittent stall loads (seen as nuisances, rather than faults is important in enhancing the performance of various fault detection schemes dealing with large input signals. For this case, a dedicated fast identification algorithm is devised. The developed FDD systems are tested on a nonlinear actuator model which is implemented in a full nonlinear aircraft simulation model. This enables the validation of the FDD system’s detection and identification characteristics under realistic conditions.

  7. Fault detection in processes represented by PLS models using an EWMA control scheme

    KAUST Repository

    Harrou, Fouzi

    2016-10-20

    Fault detection is important for effective and safe process operation. Partial least squares (PLS) has been used successfully in fault detection for multivariate processes with highly correlated variables. However, the conventional PLS-based detection metrics, such as the Hotelling\\'s T and the Q statistics are not well suited to detect small faults because they only use information about the process in the most recent observation. Exponentially weighed moving average (EWMA), however, has been shown to be more sensitive to small shifts in the mean of process variables. In this paper, a PLS-based EWMA fault detection method is proposed for monitoring processes represented by PLS models. The performance of the proposed method is compared with that of the traditional PLS-based fault detection method through a simulated example involving various fault scenarios that could be encountered in real processes. The simulation results clearly show the effectiveness of the proposed method over the conventional PLS method.

  8. Robust fault detection of wind energy conversion systems based on dynamic neural networks.

    Science.gov (United States)

    Talebi, Nasser; Sadrnia, Mohammad Ali; Darabi, Ahmad

    2014-01-01

    Occurrence of faults in wind energy conversion systems (WECSs) is inevitable. In order to detect the occurred faults at the appropriate time, avoid heavy economic losses, ensure safe system operation, prevent damage to adjacent relevant systems, and facilitate timely repair of failed components; a fault detection system (FDS) is required. Recurrent neural networks (RNNs) have gained a noticeable position in FDSs and they have been widely used for modeling of complex dynamical systems. One method for designing an FDS is to prepare a dynamic neural model emulating the normal system behavior. By comparing the outputs of the real system and neural model, incidence of the faults can be identified. In this paper, by utilizing a comprehensive dynamic model which contains both mechanical and electrical components of the WECS, an FDS is suggested using dynamic RNNs. The presented FDS detects faults of the generator's angular velocity sensor, pitch angle sensors, and pitch actuators. Robustness of the FDS is achieved by employing an adaptive threshold. Simulation results show that the proposed scheme is capable to detect the faults shortly and it has very low false and missed alarms rate.

  9. A rule-based fault detection method for air handling units

    Energy Technology Data Exchange (ETDEWEB)

    Schein, J.; Bushby, S. T.; Castro, N. S. [National Institute of Standards and Technology, Gaithersburg, MD (United States); House, J. M. [Iowa Energy Center, Ankeny, IA (United States)

    2006-07-01

    Air handling unit performance assessment rules (APAR) is a fault detection tool that uses a set of expert rules derived from mass and energy balances to detect faults in air handling units (AHUs). Control signals are used to determine the mode of operation of the AHU. A subset of the expert rules which correspond to that mode of operation are then evaluated to determine whether a fault exists. APAR is computationally simple enough that it can be embedded in commercial building automation and control systems and relies only upon the sensor data and control signals that are commonly available in these systems. APAR was tested using data sets collected from a 'hardware-in-the-loop' emulator and from several field sites. APAR was also embedded in commercial AHU controllers and tested in the emulator. (author)

  10. Fault prediction for nonlinear stochastic system with incipient faults based on particle filter and nonlinear regression.

    Science.gov (United States)

    Ding, Bo; Fang, Huajing

    2017-05-01

    This paper is concerned with the fault prediction for the nonlinear stochastic system with incipient faults. Based on the particle filter and the reasonable assumption about the incipient faults, the modified fault estimation algorithm is proposed, and the system state is estimated simultaneously. According to the modified fault estimation, an intuitive fault detection strategy is introduced. Once each of the incipient fault is detected, the parameters of which are identified by a nonlinear regression method. Then, based on the estimated parameters, the future fault signal can be predicted. Finally, the effectiveness of the proposed method is verified by the simulations of the Three-tank system. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  11. Norm based design of fault detectors

    DEFF Research Database (Denmark)

    Rank, Mike Lind; Niemann, Hans Henrik

    1999-01-01

    The design of fault detectors for fault detection and isolation (FDI) in dynamic systems is considered in this paper from a norm based point of view. An analysis of norm based threshold selection is given based on different formulations of FDI problems. Both the nominal FDI problem as well...

  12. Robust Fault Detection for a Class of Uncertain Nonlinear Systems Based on Multiobjective Optimization

    Directory of Open Access Journals (Sweden)

    Bingyong Yan

    2015-01-01

    Full Text Available A robust fault detection scheme for a class of nonlinear systems with uncertainty is proposed. The proposed approach utilizes robust control theory and parameter optimization algorithm to design the gain matrix of fault tracking approximator (FTA for fault detection. The gain matrix of FTA is designed to minimize the effects of system uncertainty on residual signals while maximizing the effects of system faults on residual signals. The design of the gain matrix of FTA takes into account the robustness of residual signals to system uncertainty and sensitivity of residual signals to system faults simultaneously, which leads to a multiobjective optimization problem. Then, the detectability of system faults is rigorously analyzed by investigating the threshold of residual signals. Finally, simulation results are provided to show the validity and applicability of the proposed approach.

  13. Fault detection and isolation in processes involving induction machines

    Energy Technology Data Exchange (ETDEWEB)

    Zell, K; Medvedev, A [Control Engineering Group, Luleaa University of Technology, Luleaa (Sweden)

    1998-12-31

    A model-based technique for fault detection and isolation in electro-mechanical systems comprising induction machines is introduced. Two coupled state observers, one for the induction machine and another for the mechanical load, are used to detect and recognize fault-specific behaviors (fault signatures) from the real-time measurements of the rotor angular velocity and terminal voltages and currents. Practical applicability of the method is verified in full-scale experiments with a conveyor belt drive at SSAB, Luleaa Works. (orig.) 3 refs.

  14. Fault detection and isolation in processes involving induction machines

    Energy Technology Data Exchange (ETDEWEB)

    Zell, K.; Medvedev, A. [Control Engineering Group, Luleaa University of Technology, Luleaa (Sweden)

    1997-12-31

    A model-based technique for fault detection and isolation in electro-mechanical systems comprising induction machines is introduced. Two coupled state observers, one for the induction machine and another for the mechanical load, are used to detect and recognize fault-specific behaviors (fault signatures) from the real-time measurements of the rotor angular velocity and terminal voltages and currents. Practical applicability of the method is verified in full-scale experiments with a conveyor belt drive at SSAB, Luleaa Works. (orig.) 3 refs.

  15. BEAT: A Web-Based Boolean Expression Fault-Based Test Case Generation Tool

    Science.gov (United States)

    Chen, T. Y.; Grant, D. D.; Lau, M. F.; Ng, S. P.; Vasa, V. R.

    2006-01-01

    BEAT is a Web-based system that generates fault-based test cases from Boolean expressions. It is based on the integration of our several fault-based test case selection strategies. The generated test cases are considered to be fault-based, because they are aiming at the detection of particular faults. For example, when the Boolean expression is in…

  16. A KPI-based process monitoring and fault detection framework for large-scale processes.

    Science.gov (United States)

    Zhang, Kai; Shardt, Yuri A W; Chen, Zhiwen; Yang, Xu; Ding, Steven X; Peng, Kaixiang

    2017-05-01

    Large-scale processes, consisting of multiple interconnected subprocesses, are commonly encountered in industrial systems, whose performance needs to be determined. A common approach to this problem is to use a key performance indicator (KPI)-based approach. However, the different KPI-based approaches are not developed with a coherent and consistent framework. Thus, this paper proposes a framework for KPI-based process monitoring and fault detection (PM-FD) for large-scale industrial processes, which considers the static and dynamic relationships between process and KPI variables. For the static case, a least squares-based approach is developed that provides an explicit link with least-squares regression, which gives better performance than partial least squares. For the dynamic case, using the kernel representation of each subprocess, an instrument variable is used to reduce the dynamic case to the static case. This framework is applied to the TE benchmark process and the hot strip mill rolling process. The results show that the proposed method can detect faults better than previous methods. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  17. A statistical-based approach for fault detection and diagnosis in a photovoltaic system

    KAUST Repository

    Garoudja, Elyes; Harrou, Fouzi; Sun, Ying; Kara, Kamel; Chouder, Aissa; Silvestre, Santiago

    2017-01-01

    This paper reports a development of a statistical approach for fault detection and diagnosis in a PV system. Specifically, the overarching goal of this work is to early detect and identify faults on the DC side of a PV system (e.g., short

  18. Active Fault Detection and Isolation for Hybrid Systems

    DEFF Research Database (Denmark)

    Gholami, Mehdi; Schiøler, Henrik; Bak, Thomas

    2009-01-01

    An algorithm for active fault detection and isolation is proposed. In order to observe the failure hidden due to the normal operation of the controllers or the systems, an optimization problem based on minimization of test signal is used. The optimization based method imposes the normal and faulty...... models predicted outputs such that their discrepancies are observable by passive fault diagnosis technique. Isolation of different faults is done by implementation a bank of Extended Kalman Filter (EKF) where the convergence criterion for EKF is confirmed by Genetic Algorithm (GA). The method is applied...

  19. A statistical-based approach for fault detection and diagnosis in a photovoltaic system

    KAUST Repository

    Garoudja, Elyes

    2017-07-10

    This paper reports a development of a statistical approach for fault detection and diagnosis in a PV system. Specifically, the overarching goal of this work is to early detect and identify faults on the DC side of a PV system (e.g., short-circuit faults; open-circuit faults; and partial shading faults). Towards this end, we apply exponentially-weighted moving average (EWMA) control chart on the residuals obtained from the one-diode model. Such a choice is motivated by the greater sensitivity of EWMA chart to incipient faults and its low-computational cost making it easy to implement in real time. Practical data from a 3.2 KWp photovoltaic plant located within an Algerian research center is used to validate the proposed approach. Results show clearly the efficiency of the developed method in monitoring PV system status.

  20. A Novel Arc Fault Detector for Early Detection of Electrical Fires.

    Science.gov (United States)

    Yang, Kai; Zhang, Rencheng; Yang, Jianhong; Liu, Canhua; Chen, Shouhong; Zhang, Fujiang

    2016-04-09

    Arc faults can produce very high temperatures and can easily ignite combustible materials; thus, they represent one of the most important causes of electrical fires. The application of arc fault detection, as an emerging early fire detection technology, is required by the National Electrical Code to reduce the occurrence of electrical fires. However, the concealment, randomness and diversity of arc faults make them difficult to detect. To improve the accuracy of arc fault detection, a novel arc fault detector (AFD) is developed in this study. First, an experimental arc fault platform is built to study electrical fires. A high-frequency transducer and a current transducer are used to measure typical load signals of arc faults and normal states. After the common features of these signals are studied, high-frequency energy and current variations are extracted as an input eigenvector for use by an arc fault detection algorithm. Then, the detection algorithm based on a weighted least squares support vector machine is designed and successfully applied in a microprocessor. Finally, an AFD is developed. The test results show that the AFD can detect arc faults in a timely manner and interrupt the circuit power supply before electrical fires can occur. The AFD is not influenced by cross talk or transient processes, and the detection accuracy is very high. Hence, the AFD can be installed in low-voltage circuits to monitor circuit states in real-time to facilitate the early detection of electrical fires.

  1. A Novel Arc Fault Detector for Early Detection of Electrical Fires

    Science.gov (United States)

    Yang, Kai; Zhang, Rencheng; Yang, Jianhong; Liu, Canhua; Chen, Shouhong; Zhang, Fujiang

    2016-01-01

    Arc faults can produce very high temperatures and can easily ignite combustible materials; thus, they represent one of the most important causes of electrical fires. The application of arc fault detection, as an emerging early fire detection technology, is required by the National Electrical Code to reduce the occurrence of electrical fires. However, the concealment, randomness and diversity of arc faults make them difficult to detect. To improve the accuracy of arc fault detection, a novel arc fault detector (AFD) is developed in this study. First, an experimental arc fault platform is built to study electrical fires. A high-frequency transducer and a current transducer are used to measure typical load signals of arc faults and normal states. After the common features of these signals are studied, high-frequency energy and current variations are extracted as an input eigenvector for use by an arc fault detection algorithm. Then, the detection algorithm based on a weighted least squares support vector machine is designed and successfully applied in a microprocessor. Finally, an AFD is developed. The test results show that the AFD can detect arc faults in a timely manner and interrupt the circuit power supply before electrical fires can occur. The AFD is not influenced by cross talk or transient processes, and the detection accuracy is very high. Hence, the AFD can be installed in low-voltage circuits to monitor circuit states in real-time to facilitate the early detection of electrical fires. PMID:27070618

  2. Novel neural networks-based fault tolerant control scheme with fault alarm.

    Science.gov (United States)

    Shen, Qikun; Jiang, Bin; Shi, Peng; Lim, Cheng-Chew

    2014-11-01

    In this paper, the problem of adaptive active fault-tolerant control for a class of nonlinear systems with unknown actuator fault is investigated. The actuator fault is assumed to have no traditional affine appearance of the system state variables and control input. The useful property of the basis function of the radial basis function neural network (NN), which will be used in the design of the fault tolerant controller, is explored. Based on the analysis of the design of normal and passive fault tolerant controllers, by using the implicit function theorem, a novel NN-based active fault-tolerant control scheme with fault alarm is proposed. Comparing with results in the literature, the fault-tolerant control scheme can minimize the time delay between fault occurrence and accommodation that is called the time delay due to fault diagnosis, and reduce the adverse effect on system performance. In addition, the FTC scheme has the advantages of a passive fault-tolerant control scheme as well as the traditional active fault-tolerant control scheme's properties. Furthermore, the fault-tolerant control scheme requires no additional fault detection and isolation model which is necessary in the traditional active fault-tolerant control scheme. Finally, simulation results are presented to demonstrate the efficiency of the developed techniques.

  3. Fault detection Based Bayesian network and MOEA/D applied to Sensorless Drive Diagnosis

    Directory of Open Access Journals (Sweden)

    Zhou Qing

    2017-01-01

    Full Text Available Sensorless Drive Diagnosis can be used to assess the process data without the need for additional cost-intensive sensor technology, and you can understand the synchronous motor and connecting parts of the damaged state. Considering the number of features involved in the process data, it is necessary to perform feature selection and reduce the data dimension in the process of fault detection. In this paper, the MOEA / D algorithm based on multi-objective optimization is used to obtain the weight vector of all the features in the original data set. It is more suitable to classify or make decisions based on these features. In order to ensure the fastness and convenience sensorless drive diagnosis, in this paper, the classic Bayesian network learning algorithm-K2 algorithm is used to study the network structure of each feature in sensorless drive, which makes the fault detection and elimination process more targeted.

  4. Fault Detection and Isolation for a Supermarket Refrigeration System - Part One

    DEFF Research Database (Denmark)

    Yang, Zhenyu; Rasmussen, Karsten B.; Kieu, Anh T.

    2011-01-01

    Fault Detection and Isolation (FDI) using the Kalman Filter (KF) technique for a supermarket refrigeration system is explored. Four types of sensor fault scenarios, namely drift, offset, freeze and hard-over, are considered for two temperature sensors, and one type of parametric fault scenario, n....... The test results show that the EKF-based FDI method generally performances better and faster than the KF-based method does. However, both methods can not handle the isolation between sensor faults and parametric fault.......Fault Detection and Isolation (FDI) using the Kalman Filter (KF) technique for a supermarket refrigeration system is explored. Four types of sensor fault scenarios, namely drift, offset, freeze and hard-over, are considered for two temperature sensors, and one type of parametric fault scenario...... isolation purpose, a bank of KFs arranged by splitting measurements is constructed for sensor fault isolation, while the Multi-Model Adaptive Estimation (MMAE) method is employed to handle parametric fault isolation. All these approaches are extended and checked by using Extended KF technique afterwards...

  5. Robust Fault Detection for Switched Fuzzy Systems With Unknown Input.

    Science.gov (United States)

    Han, Jian; Zhang, Huaguang; Wang, Yingchun; Sun, Xun

    2017-10-03

    This paper investigates the fault detection problem for a class of switched nonlinear systems in the T-S fuzzy framework. The unknown input is considered in the systems. A novel fault detection unknown input observer design method is proposed. Based on the proposed observer, the unknown input can be removed from the fault detection residual. The weighted H∞ performance level is considered to ensure the robustness. In addition, the weighted H₋ performance level is introduced, which can increase the sensibility of the proposed detection method. To verify the proposed scheme, a numerical simulation example and an electromechanical system simulation example are provided at the end of this paper.

  6. Fault diagnosis of sensor networked structures with multiple faults using a virtual beam based approach

    Science.gov (United States)

    Wang, H.; Jing, X. J.

    2017-07-01

    This paper presents a virtual beam based approach suitable for conducting diagnosis of multiple faults in complex structures with limited prior knowledge of the faults involved. The "virtual beam", a recently-proposed concept for fault detection in complex structures, is applied, which consists of a chain of sensors representing a vibration energy transmission path embedded in the complex structure. Statistical tests and adaptive threshold are particularly adopted for fault detection due to limited prior knowledge of normal operational conditions and fault conditions. To isolate the multiple faults within a specific structure or substructure of a more complex one, a 'biased running' strategy is developed and embedded within the bacterial-based optimization method to construct effective virtual beams and thus to improve the accuracy of localization. The proposed method is easy and efficient to implement for multiple fault localization with limited prior knowledge of normal conditions and faults. With extensive experimental results, it is validated that the proposed method can localize both single fault and multiple faults more effectively than the classical trust index subtract on negative add on positive (TI-SNAP) method.

  7. Fault-weighted quantification method of fault detection coverage through fault mode and effect analysis in digital I&C systems

    Energy Technology Data Exchange (ETDEWEB)

    Cho, Jaehyun; Lee, Seung Jun, E-mail: sjlee420@unist.ac.kr; Jung, Wondea

    2017-05-15

    Highlights: • We developed the fault-weighted quantification method of fault detection coverage. • The method has been applied to specific digital reactor protection system. • The unavailability of the module had 20-times difference with the traditional method. • Several experimental tests will be effectively prioritized using this method. - Abstract: The one of the most outstanding features of a digital I&C system is the use of a fault-tolerant technique. With an awareness regarding the importance of thequantification of fault detection coverage of fault-tolerant techniques, several researches related to the fault injection method were developed and employed to quantify a fault detection coverage. In the fault injection method, each injected fault has a different importance because the frequency of realization of every injected fault is different. However, there have been no previous studies addressing the importance and weighting factor of each injected fault. In this work, a new method for allocating the weighting to each injected fault using the failure mode and effect analysis data was proposed. For application, the fault-weighted quantification method has also been applied to specific digital reactor protection system to quantify the fault detection coverage. One of the major findings in an application was that we may estimate the unavailability of the specific module in digital I&C systems about 20-times smaller than real value when we use a traditional method. The other finding was that we can also classify the importance of the experimental case. Therefore, this method is expected to not only suggest an accurate quantification procedure of fault-detection coverage by weighting the injected faults, but to also contribute to an effective fault injection experiment by sorting the importance of the failure categories.

  8. Sensor fault detection and recovery in satellite attitude control

    Science.gov (United States)

    Nasrolahi, Seiied Saeed; Abdollahi, Farzaneh

    2018-04-01

    This paper proposes an integrated sensor fault detection and recovery for the satellite attitude control system. By introducing a nonlinear observer, the healthy sensor measurements are provided. Considering attitude dynamics and kinematic, a novel observer is developed to detect the fault in angular rate as well as attitude sensors individually or simultaneously. There is no limit on type and configuration of attitude sensors. By designing a state feedback based control signal and Lyapunov stability criterion, the uniformly ultimately boundedness of tracking errors in the presence of sensor faults is guaranteed. Finally, simulation results are presented to illustrate the performance of the integrated scheme.

  9. Stochastic Change Detection based on an Active Fault Diagnosis Approach

    DEFF Research Database (Denmark)

    Poulsen, Niels Kjølstad; Niemann, Hans Henrik

    2007-01-01

    The focus in this paper is on stochastic change detection applied in connection with active fault diagnosis (AFD). An auxiliary input signal is applied in AFD. This signal injection in the system will in general allow to obtain a fast change detection/isolation by considering the output or an err...

  10. Fault diagnosis and refrigerant leak detection in vapour compression refrigeration systems

    Energy Technology Data Exchange (ETDEWEB)

    Tassou, S.A.; Grace, I.N. [Brunel University, Uxbridge (United Kingdom). Department of Mechanical Engineering

    2005-08-01

    The environmental impact of refrigeration systems can be reduced by operation at higher efficiency and reduction of refrigerant leakage. Refrigerant loss contributes both directly and indirectly to global warming through inefficient system operation, increased power consumption and greenhouse gas emissions and higher maintenance costs. Existing sensor-based leak detection methods are limited by the inability to detect gradual leakage and the need for careful sensor location. There is a requirement for a real-time performance monitoring approach to leak detection and fault diagnosis which overcomes these disadvantages. This paper reports on the development of a fault diagnosis and refrigerant leak detection system based on artificial intelligence and real-time performance monitoring. The system has been used successfully to distinguish between faulty and fault free operation, steady-state and transient operation, leakage and over charge conditions. Work currently underway is aimed at testing additional fault conditions and establishing further rules to distinguish between these patterns. (author)

  11. Incipient Fault Detection for Rolling Element Bearings under Varying Speed Conditions.

    Science.gov (United States)

    Xue, Lang; Li, Naipeng; Lei, Yaguo; Li, Ningbo

    2017-06-20

    Varying speed conditions bring a huge challenge to incipient fault detection of rolling element bearings because both the change of speed and faults could lead to the amplitude fluctuation of vibration signals. Effective detection methods need to be developed to eliminate the influence of speed variation. This paper proposes an incipient fault detection method for bearings under varying speed conditions. Firstly, relative residual (RR) features are extracted, which are insensitive to the varying speed conditions and are able to reflect the degradation trend of bearings. Then, a health indicator named selected negative log-likelihood probability (SNLLP) is constructed to fuse a feature set including RR features and non-dimensional features. Finally, based on the constructed SNLLP health indicator, a novel alarm trigger mechanism is designed to detect the incipient fault. The proposed method is demonstrated using vibration signals from bearing tests and industrial wind turbines. The results verify the effectiveness of the proposed method for incipient fault detection of rolling element bearings under varying speed conditions.

  12. Automatic supervision and fault detection of PV systems based on power losses analysis

    Energy Technology Data Exchange (ETDEWEB)

    Chouder, A.; Silvestre, S. [Electronic Engineering Department, Universitat Politecnica de Catalunya, C/Jordi Girona 1-3, Campus Nord UPC, 08034 Barcelona (Spain)

    2010-10-15

    In this work, we present a new automatic supervision and fault detection procedure for PV systems, based on the power losses analysis. This automatic supervision system has been developed in Matlab and Simulink environment. It includes parameter extraction techniques to calculate main PV system parameters from monitoring data in real conditions of work, taking into account the environmental irradiance and module temperature evolution, allowing simulation of the PV system behaviour in real time. The automatic supervision method analyses the output power losses, presents in the DC side of the PV generator, capture losses. Two new power losses indicators are defined: thermal capture losses (L{sub ct}) and miscellaneous capture losses (L{sub cm}). The processing of these indicators allows the supervision system to generate a faulty signal as indicator of fault detection in the PV system operation. Two new indicators of the deviation of the DC variables respect to the simulated ones have been also defined. These indicators are the current and voltage ratios: R{sub C} and R{sub V}. Analysing both, the faulty signal and the current/voltage ratios, the type of fault can be identified. The automatic supervision system has been successfully tested experimentally. (author)

  13. Automatic supervision and fault detection of PV systems based on power losses analysis

    International Nuclear Information System (INIS)

    Chouder, A.; Silvestre, S.

    2010-01-01

    In this work, we present a new automatic supervision and fault detection procedure for PV systems, based on the power losses analysis. This automatic supervision system has been developed in Matlab and Simulink environment. It includes parameter extraction techniques to calculate main PV system parameters from monitoring data in real conditions of work, taking into account the environmental irradiance and module temperature evolution, allowing simulation of the PV system behaviour in real time. The automatic supervision method analyses the output power losses, presents in the DC side of the PV generator, capture losses. Two new power losses indicators are defined: thermal capture losses (L ct ) and miscellaneous capture losses (L cm ). The processing of these indicators allows the supervision system to generate a faulty signal as indicator of fault detection in the PV system operation. Two new indicators of the deviation of the DC variables respect to the simulated ones have been also defined. These indicators are the current and voltage ratios: R C and R V . Analysing both, the faulty signal and the current/voltage ratios, the type of fault can be identified. The automatic supervision system has been successfully tested experimentally.

  14. Integral Sensor Fault Detection and Isolation for Railway Traction Drive.

    Science.gov (United States)

    Garramiola, Fernando; Del Olmo, Jon; Poza, Javier; Madina, Patxi; Almandoz, Gaizka

    2018-05-13

    Due to the increasing importance of reliability and availability of electric traction drives in Railway applications, early detection of faults has become an important key for Railway traction drive manufacturers. Sensor faults are important sources of failures. Among the different fault diagnosis approaches, in this article an integral diagnosis strategy for sensors in traction drives is presented. Such strategy is composed of an observer-based approach for direct current (DC)-link voltage and catenary current sensors, a frequency analysis approach for motor current phase sensors and a hardware redundancy solution for speed sensors. None of them requires any hardware change requirement in the actual traction drive. All the fault detection and isolation approaches have been validated in a Hardware-in-the-loop platform comprising a Real Time Simulator and a commercial Traction Control Unit for a tram. In comparison to safety-critical systems in Aerospace applications, Railway applications do not need instantaneous detection, and the diagnosis is validated in a short time period for reliable decision. Combining the different approaches and existing hardware redundancy, an integral fault diagnosis solution is provided, to detect and isolate faults in all the sensors installed in the traction drive.

  15. Planetary Gearbox Fault Detection Using Vibration Separation Techniques

    Science.gov (United States)

    Lewicki, David G.; LaBerge, Kelsen E.; Ehinger, Ryan T.; Fetty, Jason

    2011-01-01

    Studies were performed to demonstrate the capability to detect planetary gear and bearing faults in helicopter main-rotor transmissions. The work supported the Operations Support and Sustainment (OSST) program with the U.S. Army Aviation Applied Technology Directorate (AATD) and Bell Helicopter Textron. Vibration data from the OH-58C planetary system were collected on a healthy transmission as well as with various seeded-fault components. Planetary fault detection algorithms were used with the collected data to evaluate fault detection effectiveness. Planet gear tooth cracks and spalls were detectable using the vibration separation techniques. Sun gear tooth cracks were not discernibly detectable from the vibration separation process. Sun gear tooth spall defects were detectable. Ring gear tooth cracks were only clearly detectable by accelerometers located near the crack location or directly across from the crack. Enveloping provided an effective method for planet bearing inner- and outer-race spalling fault detection.

  16. Detecting Fan Faults in refrigerated Cabinets

    DEFF Research Database (Denmark)

    Thybo, C.; Rasmussen, B.D.; Izadi-Zamanabadi, Roozbeh

    2002-01-01

    Fault detection in supermarket refrigeration systems is an important topic due to both economic and food safety reasons. If faults can be detected and diagnosed before the system drifts outside the specified operational envelope, service costs can be reduced and in extreme cases the costly discar...

  17. On-line early fault detection and diagnosis of municipal solid waste incinerators

    International Nuclear Information System (INIS)

    Zhao Jinsong; Huang Jianchao; Sun Wei

    2008-01-01

    A fault detection and diagnosis framework is proposed in this paper for early fault detection and diagnosis (FDD) of municipal solid waste incinerators (MSWIs) in order to improve the safety and continuity of production. In this framework, principal component analysis (PCA), one of the multivariate statistical technologies, is used for detecting abnormal events, while rule-based reasoning performs the fault diagnosis and consequence prediction, and also generates recommendations for fault mitigation once an abnormal event is detected. A software package, SWIFT, is developed based on the proposed framework, and has been applied in an actual industrial MSWI. The application shows that automated real-time abnormal situation management (ASM) of the MSWI can be achieved by using SWIFT, resulting in an industrially acceptable low rate of wrong diagnosis, which has resulted in improved process continuity and environmental performance of the MSWI

  18. Static Decoupling in fault detection

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik

    1998-01-01

    An algebraic approach is given for a design of a static residual weighting factor in connection with fault detection. A complete parameterization is given of the weighting factor which will minimize a given performance index......An algebraic approach is given for a design of a static residual weighting factor in connection with fault detection. A complete parameterization is given of the weighting factor which will minimize a given performance index...

  19. A Prognostic Method for Fault Detection in Wind Turbine Drivetrains

    DEFF Research Database (Denmark)

    Nejada, Amir R.; Odgaard, Peter Fogh; Gao, Zhen

    2014-01-01

    In this paper, a prognostic method is presented for fault detection in gears and bearings in wind turbine drivetrains. This method is based on angular velocity measurements from the gearbox input shaft and the output to the generator, using two additional angular velocity sensors on the intermedi......In this paper, a prognostic method is presented for fault detection in gears and bearings in wind turbine drivetrains. This method is based on angular velocity measurements from the gearbox input shaft and the output to the generator, using two additional angular velocity sensors...... bearing faults in three locations: the high-speed shaft stage, the planetary stage and the intermediate-speed shaft stage. Simulations of the faulty and fault-free cases are performed on a gearbox model implemented in multibody dynamic simulation software. The global loads on the gearbox are obtained from...

  20. Applying Parametric Fault Detection to a Mechanical System

    DEFF Research Database (Denmark)

    Felício, P.; Stoustrup, Jakob; Niemann, H.

    2002-01-01

    A way of doing parametric fault detection is described. It is based on the representation of parameter changes as linear fractional transformations (lfts). We describe a model with parametric uncertainty. Then a stabilizing controller is chosen and its robustness properties are studied via mu. Th....... The parameter changes (faults) are estimated based on estimates of the fictitious signals that enter the delta block in the lft. These signal estimators are designed by H-infinity techniques. The chosen example is an inverted pendulum....

  1. Improved nonlinear fault detection strategy based on the Hellinger distance metric: Plug flow reactor monitoring

    KAUST Repository

    Harrou, Fouzi; Madakyaru, Muddu; Sun, Ying

    2017-01-01

    Fault detection has a vital role in the process industry to enhance productivity, efficiency, and safety, and to avoid expensive maintenance. This paper proposes an innovative multivariate fault detection method that can be used for monitoring

  2. Incipient Fault Detection for Rolling Element Bearings under Varying Speed Conditions

    Directory of Open Access Journals (Sweden)

    Lang Xue

    2017-06-01

    Full Text Available Varying speed conditions bring a huge challenge to incipient fault detection of rolling element bearings because both the change of speed and faults could lead to the amplitude fluctuation of vibration signals. Effective detection methods need to be developed to eliminate the influence of speed variation. This paper proposes an incipient fault detection method for bearings under varying speed conditions. Firstly, relative residual (RR features are extracted, which are insensitive to the varying speed conditions and are able to reflect the degradation trend of bearings. Then, a health indicator named selected negative log-likelihood probability (SNLLP is constructed to fuse a feature set including RR features and non-dimensional features. Finally, based on the constructed SNLLP health indicator, a novel alarm trigger mechanism is designed to detect the incipient fault. The proposed method is demonstrated using vibration signals from bearing tests and industrial wind turbines. The results verify the effectiveness of the proposed method for incipient fault detection of rolling element bearings under varying speed conditions.

  3. Fault detection for discrete-time switched systems with sensor stuck faults and servo inputs.

    Science.gov (United States)

    Zhong, Guang-Xin; Yang, Guang-Hong

    2015-09-01

    This paper addresses the fault detection problem of switched systems with servo inputs and sensor stuck faults. The attention is focused on designing a switching law and its associated fault detection filters (FDFs). The proposed switching law uses only the current states of FDFs, which guarantees the residuals are sensitive to the servo inputs with known frequency ranges in faulty cases and robust against them in fault-free case. Thus, the arbitrarily small sensor stuck faults, including outage faults can be detected in finite-frequency domain. The levels of sensitivity and robustness are measured in terms of the finite-frequency H- index and l2-gain. Finally, the switching law and FDFs are obtained by the solution of a convex optimization problem. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  4. Fault and meal detection by redundant continuous glucose monitors and the unscented Kalman filter

    DEFF Research Database (Denmark)

    Mahmoudi, Zeinab; Nørgaard, Kirsten; Poulsen, Niels Kjølstad

    2017-01-01

    The purpose of this study is to develop a method for detecting and compensating the anomalies of continuous glucose monitoring (CGM) sensors as well as detecting unannounced meals. Both features, sensor fault detection/correction and meal detection, are necessary to have a reliable artificial pan...... is corrupted by PISA. The fault isolator can detect 199 out of 200 unannounced meals. The average change in the glucose concentrations between the meals and the detection time points is 46.3 mg/dL.......The purpose of this study is to develop a method for detecting and compensating the anomalies of continuous glucose monitoring (CGM) sensors as well as detecting unannounced meals. Both features, sensor fault detection/correction and meal detection, are necessary to have a reliable artificial...... from the two fault detectors differentiates between a sensor fault and an unannounced meal appearing as an anomaly in the CGM data. If the fault isolator indicates a sensor fault, a method based on the covariance matching technique tunes the covariance of the measurement noise associated...

  5. Information Based Fault Diagnosis

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2008-01-01

    Fault detection and isolation, (FDI) of parametric faults in dynamic systems will be considered in this paper. An active fault diagnosis (AFD) approach is applied. The fault diagnosis will be investigated with respect to different information levels from the external inputs to the systems. These ...

  6. A fault detection and diagnosis in a PWR steam generator

    International Nuclear Information System (INIS)

    Park, Seung Yub

    1991-01-01

    The purpose of this study is to develop a fault detection and diagnosis scheme that can monitor process fault and instrument fault of a steam generator. The suggested scheme consists of a Kalman filter and two bias estimators. Method of detecting process and instrument fault in a steam generator uses the mean test on the residual sequence of Kalman filter, designed for the unfailed system, to make a fault decision. Once a fault is detected, two bias estimators are driven to estimate the fault and to discriminate process fault and instrument fault. In case of process fault, the fault diagnosis of outlet temperature, feed-water heater and main steam control valve is considered. In instrument fault, the fault diagnosis of steam generator's three instruments is considered. Computer simulation tests show that on-line prompt fault detection and diagnosis can be performed very successfully.(Author)

  7. An imbalance fault detection method based on data normalization and EMD for marine current turbines.

    Science.gov (United States)

    Zhang, Milu; Wang, Tianzhen; Tang, Tianhao; Benbouzid, Mohamed; Diallo, Demba

    2017-05-01

    This paper proposes an imbalance fault detection method based on data normalization and Empirical Mode Decomposition (EMD) for variable speed direct-drive Marine Current Turbine (MCT) system. The method is based on the MCT stator current under the condition of wave and turbulence. The goal of this method is to extract blade imbalance fault feature, which is concealed by the supply frequency and the environment noise. First, a Generalized Likelihood Ratio Test (GLRT) detector is developed and the monitoring variable is selected by analyzing the relationship between the variables. Then, the selected monitoring variable is converted into a time series through data normalization, which makes the imbalance fault characteristic frequency into a constant. At the end, the monitoring variable is filtered out by EMD method to eliminate the effect of turbulence. The experiments show that the proposed method is robust against turbulence through comparing the different fault severities and the different turbulence intensities. Comparison with other methods, the experimental results indicate the feasibility and efficacy of the proposed method. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  8. Fault Detection of Aircraft Cable via Spread Spectrum Time Domain Reflectometry

    Directory of Open Access Journals (Sweden)

    Xudong SHI

    2014-03-01

    Full Text Available As the airplane cable fault detection based on TDR (time domain reflectometry is affected easily by various noise signals, which makes the reflected signal attenuate and distort heavily, failing to locate the fault. In order to solve these problems, a method of spread spectrum time domain reflectometry (SSTDR is introduced in this paper, taking the advantage of the sharp peak of correlation function. The test signal is generated from ML sequence (MLS modulated by sine wave in the same frequency. Theoretically, the test signal has the very high immunity of noise, which can be applied with excellent precision to fault location on the aircraft cable. In this paper, the method of SSTDR was normally simulated in MATLAB. Then, an experimental setup, based on LabVIEW, was organized to detect and locate the fault on the aircraft cable. It has been demonstrated that SSTDR has the high immunity of noise, reducing some detection errors effectively.

  9. Radial basis function neural network in fault detection of automotive ...

    African Journals Online (AJOL)

    Radial basis function neural network in fault detection of automotive engines. ... Five faults have been simulated on the MVEM, including three sensor faults, one component fault and one actuator fault. The three sensor faults ... Keywords: Automotive engine, independent RBFNN model, RBF neural network, fault detection

  10. Distributed Fault Detection for a Class of Nonlinear Stochastic Systems

    Directory of Open Access Journals (Sweden)

    Bingyong Yan

    2014-01-01

    Full Text Available A novel distributed fault detection strategy for a class of nonlinear stochastic systems is presented. Different from the existing design procedures for fault detection, a novel fault detection observer, which consists of a nonlinear fault detection filter and a consensus filter, is proposed to detect the nonlinear stochastic systems faults. Firstly, the outputs of the nonlinear stochastic systems act as inputs of a consensus filter. Secondly, a nonlinear fault detection filter is constructed to provide estimation of unmeasurable system states and residual signals using outputs of the consensus filter. Stability analysis of the consensus filter is rigorously investigated. Meanwhile, the design procedures of the nonlinear fault detection filter are given in terms of linear matrix inequalities (LMIs. Taking the influence of the system stochastic noises into consideration, an outstanding feature of the proposed scheme is that false alarms can be reduced dramatically. Finally, simulation results are provided to show the feasibility and effectiveness of the proposed fault detection approach.

  11. Fault Detection for Shipboard Monitoring and Decision Support Systems

    DEFF Research Database (Denmark)

    Lajic, Zoran; Nielsen, Ulrik Dam

    2009-01-01

    In this paper a basic idea of a fault-tolerant monitoring and decision support system will be explained. Fault detection is an important part of the fault-tolerant design for in-service monitoring and decision support systems for ships. In the paper, a virtual example of fault detection...... will be presented for a containership with a real decision support system onboard. All possible faults can be simulated and detected using residuals and the generalized likelihood ratio (GLR) algorithm....

  12. Integration of Fault Detection and Isolation with Control Using Neuro-fuzzy Scheme

    Directory of Open Access Journals (Sweden)

    A. Asokan

    2009-10-01

    Full Text Available In this paper an algorithms is developed for fault diagnosis and fault tolerant control strategy for nonlinear systems subjected to an unknown time-varying fault. At first, the design of fault diagnosis scheme is performed using model based fault detection technique. The neuro-fuzzy chi-square scheme is applied for fault detection and isolation. The fault magnitude and time of occurrence of fault is obtained through neuro-fuzzy chi-square scheme. The estimated magnitude of the fault magnitude is normalized and used by the feed-forward control algorithm to make appropriate changes in the manipulated variable to keep the controlled variable near its set value. The feed-forward controller acts along with feed-back controller to control the multivariable system. The performance of the proposed scheme is applied to a three- tank process for various types of fault inputs to show the effectiveness of the proposed approach.

  13. Multiple-Fault Detection Methodology Based on Vibration and Current Analysis Applied to Bearings in Induction Motors and Gearboxes on the Kinematic Chain

    Directory of Open Access Journals (Sweden)

    Juan Jose Saucedo-Dorantes

    2016-01-01

    Full Text Available Gearboxes and induction motors are important components in industrial applications and their monitoring condition is critical in the industrial sector so as to reduce costs and maintenance downtimes. There are several techniques associated with the fault diagnosis in rotating machinery; however, vibration and stator currents analysis are commonly used due to their proven reliability. Indeed, vibration and current analysis provide fault condition information by means of the fault-related spectral component identification. This work presents a methodology based on vibration and current analysis for the diagnosis of wear in a gearbox and the detection of bearing defect in an induction motor both linked to the same kinematic chain; besides, the location of the fault-related components for analysis is supported by the corresponding theoretical models. The theoretical models are based on calculation of characteristic gearbox and bearings fault frequencies, in order to locate the spectral components of the faults. In this work, the influence of vibrations over the system is observed by performing motor current signal analysis to detect the presence of faults. The obtained results show the feasibility of detecting multiple faults in a kinematic chain, making the proposed methodology suitable to be used in the application of industrial machinery diagnosis.

  14. Broken Bar Fault Detection in IM Operating Under No-Load Condition

    Directory of Open Access Journals (Sweden)

    RELJIC, D.

    2016-11-01

    Full Text Available This paper presents a novel method for broken rotor bar detection in a squirrel-cage induction motor (IM. The proposed method applies a single-phase AC voltage as a test signal on motor terminals, resulting in a stator backward-rotating magnetic field. The field ultimately causes additional current components in the stator windings whose magnitudes depend on the broken bar fault severity, even if the motor is unloaded. This allows robust broken bar fault detection based only on standard motor current signature analysis (MCSA technique. The proposed fault detection method is at first verified via simulations, using an IM model based on finite element analysis (FEA and multiple coupled circuit approach (MCCA. The subsequent experimental investigations have shown good agreement with both theoretical predictions and simulation results.

  15. FPGA-based fully digital fast power switch fault detection and compensation for three-phase shunt active filters

    Energy Technology Data Exchange (ETDEWEB)

    Karimi, S.; Saadate, S. [Groupe de Recherche en Electrotechnique et Electronique de Nancy, GREEN-UHP, CNRS UMR 7037 (France); Poure, P. [Laboratoire d' Instrumentation Electronique de Nancy, LIEN, EA 3440, France Nancy Universite - Universite Henri Poincare de Nancy I, BP 239, 54506 Vandoeuvre les Nancy cedex (France)

    2008-11-15

    This paper discusses the design, implementation, experimental validation and performances of a fully digital fast power switch fault detection and compensation for three-phase shunt active power filters. The approach introduced in this paper minimizes the time interval between the fault occurrence and its diagnosis. This paper demonstrates the possibility to detect a faulty switch of the active filter in less than 10 {mu}s by using simultaneously a ''time criterion'' and a ''voltage criterion''. In order to attain this fast detection time a FPGA (Field Programmable Gate Array) is used. The other feature introduced in this approach is that the control scheme used to compensate the current load harmonics and fault tolerant scheme are both programmed in only one FPGA. ''FPGA in the loop'' prototyping results and fully experimental results based on a real active power filter verify satisfactory performances of the proposed method. (author)

  16. New algorithm to detect modules in a fault tree for a PSA

    International Nuclear Information System (INIS)

    Jung, Woo Sik

    2015-01-01

    A module or independent subtree is a part of a fault tree whose child gates or basic events are not repeated in the remaining part of the fault tree. Modules are necessarily employed in order to reduce the computational costs of fault tree quantification. This paper presents a new linear time algorithm to detect modules of large fault trees. The size of cut sets can be substantially reduced by replacing independent subtrees in a fault tree with super-components. Chatterjee and Birnbaum developed properties of modules, and demonstrated their use in the fault tree analysis. Locks expanded the concept of modules to non-coherent fault trees. Independent subtrees were manually identified while coding a fault tree for computer analysis. However, nowadays, the independent subtrees are automatically identified by the fault tree solver. A Dutuit and Rauzy (DR) algorithm to detect modules of a fault tree for coherent or non-coherent fault tree was proposed in 1996. It has been well known that this algorithm quickly detects modules since it is a linear time algorithm. The new algorithm minimizes computational memory and quickly detects modules. Furthermore, it can be easily implemented into industry fault tree solvers that are based on traditional Boolean algebra, binary decision diagrams (BDDs), or Zero-suppressed BDDs. The new algorithm employs only two scalar variables in Eqs. to that are volatile information. After finishing the traversal and module detection of each node, the volatile information is destroyed. Thus, the new algorithm does not employ any other additional computational memory and operations. It is recommended that this method be implemented into fault tree solvers for efficient probabilistic safety assessment (PSA) of nuclear power plants

  17. New algorithm to detect modules in a fault tree for a PSA

    Energy Technology Data Exchange (ETDEWEB)

    Jung, Woo Sik [Sejong University, Seoul (Korea, Republic of)

    2015-05-15

    A module or independent subtree is a part of a fault tree whose child gates or basic events are not repeated in the remaining part of the fault tree. Modules are necessarily employed in order to reduce the computational costs of fault tree quantification. This paper presents a new linear time algorithm to detect modules of large fault trees. The size of cut sets can be substantially reduced by replacing independent subtrees in a fault tree with super-components. Chatterjee and Birnbaum developed properties of modules, and demonstrated their use in the fault tree analysis. Locks expanded the concept of modules to non-coherent fault trees. Independent subtrees were manually identified while coding a fault tree for computer analysis. However, nowadays, the independent subtrees are automatically identified by the fault tree solver. A Dutuit and Rauzy (DR) algorithm to detect modules of a fault tree for coherent or non-coherent fault tree was proposed in 1996. It has been well known that this algorithm quickly detects modules since it is a linear time algorithm. The new algorithm minimizes computational memory and quickly detects modules. Furthermore, it can be easily implemented into industry fault tree solvers that are based on traditional Boolean algebra, binary decision diagrams (BDDs), or Zero-suppressed BDDs. The new algorithm employs only two scalar variables in Eqs. to that are volatile information. After finishing the traversal and module detection of each node, the volatile information is destroyed. Thus, the new algorithm does not employ any other additional computational memory and operations. It is recommended that this method be implemented into fault tree solvers for efficient probabilistic safety assessment (PSA) of nuclear power plants.

  18. An adaptive demodulation approach for bearing fault detection based on adaptive wavelet filtering and spectral subtraction

    Science.gov (United States)

    Zhang, Yan; Tang, Baoping; Liu, Ziran; Chen, Rengxiang

    2016-02-01

    Fault diagnosis of rolling element bearings is important for improving mechanical system reliability and performance. Vibration signals contain a wealth of complex information useful for state monitoring and fault diagnosis. However, any fault-related impulses in the original signal are often severely tainted by various noises and the interfering vibrations caused by other machine elements. Narrow-band amplitude demodulation has been an effective technique to detect bearing faults by identifying bearing fault characteristic frequencies. To achieve this, the key step is to remove the corrupting noise and interference, and to enhance the weak signatures of the bearing fault. In this paper, a new method based on adaptive wavelet filtering and spectral subtraction is proposed for fault diagnosis in bearings. First, to eliminate the frequency associated with interfering vibrations, the vibration signal is bandpass filtered with a Morlet wavelet filter whose parameters (i.e. center frequency and bandwidth) are selected in separate steps. An alternative and efficient method of determining the center frequency is proposed that utilizes the statistical information contained in the production functions (PFs). The bandwidth parameter is optimized using a local ‘greedy’ scheme along with Shannon wavelet entropy criterion. Then, to further reduce the residual in-band noise in the filtered signal, a spectral subtraction procedure is elaborated after wavelet filtering. Instead of resorting to a reference signal as in the majority of papers in the literature, the new method estimates the power spectral density of the in-band noise from the associated PF. The effectiveness of the proposed method is validated using simulated data, test rig data, and vibration data recorded from the transmission system of a helicopter. The experimental results and comparisons with other methods indicate that the proposed method is an effective approach to detecting the fault-related impulses

  19. An adaptive demodulation approach for bearing fault detection based on adaptive wavelet filtering and spectral subtraction

    International Nuclear Information System (INIS)

    Zhang, Yan; Tang, Baoping; Chen, Rengxiang; Liu, Ziran

    2016-01-01

    Fault diagnosis of rolling element bearings is important for improving mechanical system reliability and performance. Vibration signals contain a wealth of complex information useful for state monitoring and fault diagnosis. However, any fault-related impulses in the original signal are often severely tainted by various noises and the interfering vibrations caused by other machine elements. Narrow-band amplitude demodulation has been an effective technique to detect bearing faults by identifying bearing fault characteristic frequencies. To achieve this, the key step is to remove the corrupting noise and interference, and to enhance the weak signatures of the bearing fault. In this paper, a new method based on adaptive wavelet filtering and spectral subtraction is proposed for fault diagnosis in bearings. First, to eliminate the frequency associated with interfering vibrations, the vibration signal is bandpass filtered with a Morlet wavelet filter whose parameters (i.e. center frequency and bandwidth) are selected in separate steps. An alternative and efficient method of determining the center frequency is proposed that utilizes the statistical information contained in the production functions (PFs). The bandwidth parameter is optimized using a local ‘greedy’ scheme along with Shannon wavelet entropy criterion. Then, to further reduce the residual in-band noise in the filtered signal, a spectral subtraction procedure is elaborated after wavelet filtering. Instead of resorting to a reference signal as in the majority of papers in the literature, the new method estimates the power spectral density of the in-band noise from the associated PF. The effectiveness of the proposed method is validated using simulated data, test rig data, and vibration data recorded from the transmission system of a helicopter. The experimental results and comparisons with other methods indicate that the proposed method is an effective approach to detecting the fault-related impulses

  20. Nondestructive detection system of faults in fuses using radioisotope

    International Nuclear Information System (INIS)

    Goncalves, D.

    1973-01-01

    A system is developed to show the viability of non-destructive detection of the faults of explosive safety fuses which are manufactured by Fabrica da Estrela do Ministerio do Exercito. The faults are detected by an ion-chamber based on the variation of the intensity of the beta particles that penetrate the fuse which passes through a collimator. The beta particles are emitted by Strontium-90 + Yttrium-90 encapsulated in either stainless steel or aluminum. The concept of 'bucking Voltage' is applied to differentiate electronically the signal generated by the ion-chamber. (author)

  1. Evaluation of MEMS-Based Wireless Accelerometer Sensors in Detecting Gear Tooth Faults in Helicopter Transmissions

    Science.gov (United States)

    Lewicki, David George; Lambert, Nicholas A.; Wagoner, Robert S.

    2015-01-01

    The diagnostics capability of micro-electro-mechanical systems (MEMS) based rotating accelerometer sensors in detecting gear tooth crack failures in helicopter main-rotor transmissions was evaluated. MEMS sensors were installed on a pre-notched OH-58C spiral-bevel pinion gear. Endurance tests were performed and the gear was run to tooth fracture failure. Results from the MEMS sensor were compared to conventional accelerometers mounted on the transmission housing. Most of the four stationary accelerometers mounted on the gear box housing and most of the CI's used gave indications of failure at the end of the test. The MEMS system performed well and lasted the entire test. All MEMS accelerometers gave an indication of failure at the end of the test. The MEMS systems performed as well, if not better, than the stationary accelerometers mounted on the gear box housing with regards to gear tooth fault detection. For both the MEMS sensors and stationary sensors, the fault detection time was not much sooner than the actual tooth fracture time. The MEMS sensor spectrum data showed large first order shaft frequency sidebands due to the measurement rotating frame of reference. The method of constructing a pseudo tach signal from periodic characteristics of the vibration data was successful in deriving a TSA signal without an actual tach and proved as an effective way to improve fault detection for the MEMS.

  2. Fiber Bragg Grating Sensor for Fault Detection in Radial and Network Transmission Lines

    Directory of Open Access Journals (Sweden)

    Mehdi Shadaram

    2010-10-01

    Full Text Available In this paper, a fiber optic based sensor capable of fault detection in both radial and network overhead transmission power line systems is investigated. Bragg wavelength shift is used to measure the fault current and detect fault in power systems. Magnetic fields generated by currents in the overhead transmission lines cause a strain in magnetostrictive material which is then detected by Fiber Bragg Grating (FBG. The Fiber Bragg interrogator senses the reflected FBG signals, and the Bragg wavelength shift is calculated and the signals are processed. A broadband light source in the control room scans the shift in the reflected signal. Any surge in the magnetic field relates to an increased fault current at a certain location. Also, fault location can be precisely defined with an artificial neural network (ANN algorithm. This algorithm can be easily coordinated with other protective devices. It is shown that the faults in the overhead transmission line cause a detectable wavelength shift on the reflected signal of FBG and can be used to detect and classify different kind of faults. The proposed method has been extensively tested by simulation and results confirm that the proposed scheme is able to detect different kinds of fault in both radial and network system.

  3. Optimal Robust Fault Detection for Linear Discrete Time Systems

    Directory of Open Access Journals (Sweden)

    Nike Liu

    2008-01-01

    Full Text Available This paper considers robust fault-detection problems for linear discrete time systems. It is shown that the optimal robust detection filters for several well-recognized robust fault-detection problems, such as ℋ−/ℋ∞, ℋ2/ℋ∞, and ℋ∞/ℋ∞ problems, are the same and can be obtained by solving a standard algebraic Riccati equation. Optimal filters are also derived for many other optimization criteria and it is shown that some well-studied and seeming-sensible optimization criteria for fault-detection filter design could lead to (optimal but useless fault-detection filters.

  4. Data driven fault detection and isolation: a wind turbine scenario

    Directory of Open Access Journals (Sweden)

    Rubén Francisco Manrique Piramanrique

    2015-04-01

    Full Text Available One of the greatest drawbacks in wind energy generation is the high maintenance cost associated to mechanical faults. This problem becomes more evident in utility scale wind turbines, where the increased size and nominal capacity comes with additional problems associated with structural vibrations and aeroelastic effects in the blades. Due to the increased operation capability, it is imperative to detect system degradation and faults in an efficient manner, maintaining system integrity, reliability and reducing operation costs. This paper presents a comprehensive comparison of four different Fault Detection and Isolation (FDI filters based on “Data Driven” (DD techniques. In order to enhance FDI performance, a multi-level strategy is used where:  the first level detects the occurrence of any given fault (detection, while  the second identifies the source of the fault (isolation. Four different DD classification techniques (namely Support Vector Machines, Artificial Neural Networks, K Nearest Neighbors and Gaussian Mixture Models were studied and compared for each of the proposed classification levels. The best strategy at each level could be selected to build the final data driven FDI system. The performance of the proposed scheme is evaluated on a benchmark model of a commercial wind turbine. 

  5. EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis

    Science.gov (United States)

    Žvokelj, Matej; Zupan, Samo; Prebil, Ivan

    2016-05-01

    A novel multivariate and multiscale statistical process monitoring method is proposed with the aim of detecting incipient failures in large slewing bearings, where subjective influence plays a minor role. The proposed method integrates the strengths of the Independent Component Analysis (ICA) multivariate monitoring approach with the benefits of Ensemble Empirical Mode Decomposition (EEMD), which adaptively decomposes signals into different time scales and can thus cope with multiscale system dynamics. The method, which was named EEMD-based multiscale ICA (EEMD-MSICA), not only enables bearing fault detection but also offers a mechanism of multivariate signal denoising and, in combination with the Envelope Analysis (EA), a diagnostic tool. The multiscale nature of the proposed approach makes the method convenient to cope with data which emanate from bearings in complex real-world rotating machinery and frequently represent the cumulative effect of many underlying phenomena occupying different regions in the time-frequency plane. The efficiency of the proposed method was tested on simulated as well as real vibration and Acoustic Emission (AE) signals obtained through conducting an accelerated run-to-failure lifetime experiment on a purpose-built laboratory slewing bearing test stand. The ability to detect and locate the early-stage rolling-sliding contact fatigue failure of the bearing indicates that AE and vibration signals carry sufficient information on the bearing condition and that the developed EEMD-MSICA method is able to effectively extract it, thereby representing a reliable bearing fault detection and diagnosis strategy.

  6. Event-Triggered Fault Detection of Nonlinear Networked Systems.

    Science.gov (United States)

    Li, Hongyi; Chen, Ziran; Wu, Ligang; Lam, Hak-Keung; Du, Haiping

    2017-04-01

    This paper investigates the problem of fault detection for nonlinear discrete-time networked systems under an event-triggered scheme. A polynomial fuzzy fault detection filter is designed to generate a residual signal and detect faults in the system. A novel polynomial event-triggered scheme is proposed to determine the transmission of the signal. A fault detection filter is designed to guarantee that the residual system is asymptotically stable and satisfies the desired performance. Polynomial approximated membership functions obtained by Taylor series are employed for filtering analysis. Furthermore, sufficient conditions are represented in terms of sum of squares (SOSs) and can be solved by SOS tools in MATLAB environment. A numerical example is provided to demonstrate the effectiveness of the proposed results.

  7. Aircraft Attitude Distributed Fault-tolerant Control Based on Dynamic Actuator

    Directory of Open Access Journals (Sweden)

    Zhou Hong-Cheng

    2014-09-01

    Full Text Available For attitude control system, based on decentralized fault-tolerant control framework, actuators damage and stuck fault detection and identification unit are designed for the flight control system. And observer-based auxiliary system unit is also designed. The auxiliary system implies control surface damage faults and disturbances information. Firstly, we give the attitude control system under actuator stuck, lose of effectiveness, and control surface damages faults. Secondly, a multi-observer is designed for actuator fault detection and identification using a decision-making mechanism to determine current actuator failure modes. Then, an adaptive sliding mode observer is designed for implicit control surface damages and interference information. The reconfigurable controller can achieve fault tolerant using the information of adaptive sliding mode observer. Finally, the simulation results show the effectiveness of the proposed method.

  8. Fault Detection in Coal Mills used in Power Plants

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Mataji, Babak

    2006-01-01

    In order to achieve high performance and efficiency of coal-fired power plants, it is highly important to control the coal flow into the furnace in the power plant. This means suppression of disturbances and force the coal mill to deliver the required coal flow, as well as monitor the coal mill...... in order to detect faults in the coal mill when they emerge. This paper deals with the second objective. Based on a simple dynamic model of the energy balance a residual is formed for the coal mill. An optimal unknown input observer is designed to estimate this residual. The estimated residual is following...... tested on measured data of a fault in a coal mill, it can hereby be concluded that this residual is very useful for detecting faults in the coal mill....

  9. A measurement-based fault detection approach applied to monitor robots swarm

    KAUST Repository

    Khaldi, Belkacem; Harrou, Fouzi; Sun, Ying; Cherif, Foudil

    2017-01-01

    present an innovative data-driven fault detection method for monitoring robots swarm. The method combines the flexibility of principal component analysis (PCA) models and the greater sensitivity of the exponentially-weighted moving average control chart

  10. Detection of Eccentricity Faults in Five-Phase Ferrite-PM Assisted Synchronous Reluctance Machines

    Directory of Open Access Journals (Sweden)

    Carlos López-Torres

    2017-05-01

    Full Text Available Air gap eccentricity faults in five-phase ferrite-assisted synchronous reluctance motors (fPMa-SynRMs tend to distort the magnetic flux in the air gap, which in turn affects the spectral content of both the stator currents and the ZSVC (zero-sequence voltage component. However, there is a lack of research dealing with the topic of fault diagnosis in multi-phase PMa-SynRMs, and in particular, those focused on detecting eccentricity faults. An analysis of the spectral components of the line currents and the ZSVC allows the development of fault diagnosis algorithms to detect eccentricity faults. The effect of the operating conditions is also analyzed, since this paper shows that it has a non-negligible impact on the effectivity and sensitivity of the diagnosis based on an analysis of the stator currents and the ZSVC. To this end, different operating conditions are analyzed. The paper also evaluates the influence of the operating conditions on the harmonic content of the line currents and the ZSVC, and determines the most suitable operating conditions to enhance the sensitivity of the analyzed methods. Finally, fault indicators employed to detect eccentricity faults, which are based on the spectral content of the stator currents and the ZSVC, are derived and their performance is assessed. The approach presented in this work may be useful for developing fault diagnosis strategies based on the acquisition and subsequent analysis and interpretation of the spectral content of the line currents and the ZSVC.

  11. Machine Fault Detection Based on Filter Bank Similarity Features Using Acoustic and Vibration Analysis

    Directory of Open Access Journals (Sweden)

    Mauricio Holguín-Londoño

    2016-01-01

    Full Text Available Vibration and acoustic analysis actively support the nondestructive and noninvasive fault diagnostics of rotating machines at early stages. Nonetheless, the acoustic signal is less used because of its vulnerability to external interferences, hindering an efficient and robust analysis for condition monitoring (CM. This paper presents a novel methodology to characterize different failure signatures from rotating machines using either acoustic or vibration signals. Firstly, the signal is decomposed into several narrow-band spectral components applying different filter bank methods such as empirical mode decomposition, wavelet packet transform, and Fourier-based filtering. Secondly, a feature set is built using a proposed similarity measure termed cumulative spectral density index and used to estimate the mutual statistical dependence between each bandwidth-limited component and the raw signal. Finally, a classification scheme is carried out to distinguish the different types of faults. The methodology is tested in two laboratory experiments, including turbine blade degradation and rolling element bearing faults. The robustness of our approach is validated contaminating the signal with several levels of additive white Gaussian noise, obtaining high-performance outcomes that make the usage of vibration, acoustic, and vibroacoustic measurements in different applications comparable. As a result, the proposed fault detection based on filter bank similarity features is a promising methodology to implement in CM of rotating machinery, even using measurements with low signal-to-noise ratio.

  12. Incipient fault detection and identification in process systems using accelerating neural network learning

    International Nuclear Information System (INIS)

    Parlos, A.G.; Muthusami, J.; Atiya, A.F.

    1994-01-01

    The objective of this paper is to present the development and numerical testing of a robust fault detection and identification (FDI) system using artificial neural networks (ANNs), for incipient (slowly developing) faults occurring in process systems. The challenge in using ANNs in FDI systems arises because of one's desire to detect faults of varying severity, faults from noisy sensors, and multiple simultaneous faults. To address these issues, it becomes essential to have a learning algorithm that ensures quick convergence to a high level of accuracy. A recently developed accelerated learning algorithm, namely a form of an adaptive back propagation (ABP) algorithm, is used for this purpose. The ABP algorithm is used for the development of an FDI system for a process composed of a direct current motor, a centrifugal pump, and the associated piping system. Simulation studies indicate that the FDI system has significantly high sensitivity to incipient fault severity, while exhibiting insensitivity to sensor noise. For multiple simultaneous faults, the FDI system detects the fault with the predominant signature. The major limitation of the developed FDI system is encountered when it is subjected to simultaneous faults with similar signatures. During such faults, the inherent limitation of pattern-recognition-based FDI methods becomes apparent. Thus, alternate, more sophisticated FDI methods become necessary to address such problems. Even though the effectiveness of pattern-recognition-based FDI methods using ANNs has been demonstrated, further testing using real-world data is necessary

  13. Vibration-Based Adaptive Novelty Detection Method for Monitoring Faults in a Kinematic Chain

    Directory of Open Access Journals (Sweden)

    Jesus Adolfo Cariño-Corrales

    2016-01-01

    Full Text Available This paper presents an adaptive novelty detection methodology applied to a kinematic chain for the monitoring of faults. The proposed approach has the premise that only information of the healthy operation of the machine is initially available and fault scenarios will eventually develop. This approach aims to cover some of the challenges presented when condition monitoring is applied under a continuous learning framework. The structure of the method is divided into two recursive stages: first, an offline stage for initialization and retraining of the feature reduction and novelty detection modules and, second, an online monitoring stage to continuously assess the condition of the machine. Contrary to classical static feature reduction approaches, the proposed method reformulates the features by employing first a Laplacian Score ranking and then the Fisher Score ranking for retraining. The proposed methodology is validated experimentally by monitoring the vibration measurements of a kinematic chain driven by an induction motor. Two faults are induced in the motor to validate the method performance to detect anomalies and adapt the feature reduction and novelty detection modules to the new information. The obtained results show the advantages of employing an adaptive approach for novelty detection and feature reduction making the proposed method suitable for industrial machinery diagnosis applications.

  14. Fault Detection Using the Clustering-kNN Rule for Gas Sensor Arrays

    Directory of Open Access Journals (Sweden)

    Jingli Yang

    2016-12-01

    Full Text Available The k-nearest neighbour (kNN rule, which naturally handles the possible non-linearity of data, is introduced to solve the fault detection problem of gas sensor arrays. In traditional fault detection methods based on the kNN rule, the detection process of each new test sample involves all samples in the entire training sample set. Therefore, these methods can be computation intensive in monitoring processes with a large volume of variables and training samples and may be impossible for real-time monitoring. To address this problem, a novel clustering-kNN rule is presented. The landmark-based spectral clustering (LSC algorithm, which has low computational complexity, is employed to divide the entire training sample set into several clusters. Further, the kNN rule is only conducted in the cluster that is nearest to the test sample; thus, the efficiency of the fault detection methods can be enhanced by reducing the number of training samples involved in the detection process of each test sample. The performance of the proposed clustering-kNN rule is fully verified in numerical simulations with both linear and non-linear models and a real gas sensor array experimental system with different kinds of faults. The results of simulations and experiments demonstrate that the clustering-kNN rule can greatly enhance both the accuracy and efficiency of fault detection methods and provide an excellent solution to reliable and real-time monitoring of gas sensor arrays.

  15. Fault Detection Using the Clustering-kNN Rule for Gas Sensor Arrays

    Science.gov (United States)

    Yang, Jingli; Sun, Zhen; Chen, Yinsheng

    2016-01-01

    The k-nearest neighbour (kNN) rule, which naturally handles the possible non-linearity of data, is introduced to solve the fault detection problem of gas sensor arrays. In traditional fault detection methods based on the kNN rule, the detection process of each new test sample involves all samples in the entire training sample set. Therefore, these methods can be computation intensive in monitoring processes with a large volume of variables and training samples and may be impossible for real-time monitoring. To address this problem, a novel clustering-kNN rule is presented. The landmark-based spectral clustering (LSC) algorithm, which has low computational complexity, is employed to divide the entire training sample set into several clusters. Further, the kNN rule is only conducted in the cluster that is nearest to the test sample; thus, the efficiency of the fault detection methods can be enhanced by reducing the number of training samples involved in the detection process of each test sample. The performance of the proposed clustering-kNN rule is fully verified in numerical simulations with both linear and non-linear models and a real gas sensor array experimental system with different kinds of faults. The results of simulations and experiments demonstrate that the clustering-kNN rule can greatly enhance both the accuracy and efficiency of fault detection methods and provide an excellent solution to reliable and real-time monitoring of gas sensor arrays. PMID:27929412

  16. System and method for bearing fault detection using stator current noise cancellation

    Science.gov (United States)

    Zhou, Wei; Lu, Bin; Habetler, Thomas G.; Harley, Ronald G.; Theisen, Peter J.

    2010-08-17

    A system and method for detecting incipient mechanical motor faults by way of current noise cancellation is disclosed. The system includes a controller configured to detect indicia of incipient mechanical motor faults. The controller further includes a processor programmed to receive a baseline set of current data from an operating motor and define a noise component in the baseline set of current data. The processor is also programmed to repeatedly receive real-time operating current data from the operating motor and remove the noise component from the operating current data in real-time to isolate any fault components present in the operating current data. The processor is then programmed to generate a fault index for the operating current data based on any isolated fault components.

  17. GLRT Based Anomaly Detection for Sensor Network Monitoring

    KAUST Repository

    Harrou, Fouzi

    2015-12-07

    Proper operation of antenna arrays requires continuously monitoring their performances. When a fault occurs in an antenna array, the radiation pattern changes and can significantly deviate from the desired design performance specifications. In this paper, the problem of fault detection in linear antenna arrays is addressed within a statistical framework. Specifically, a statistical fault detection method based on the generalized likelihood ratio (GLR) principle is utilized for detecting potential faults in linear antenna arrays. The proposed method relies on detecting deviations in the radiation pattern of the monitored array with respect to a reference (fault-free) one. To assess the abilities of the GLR based fault detection method, three case studies involving different types of faults have been performed. The simulation results clearly illustrate the effectiveness of the GLR-based fault detection method in monitoring the performance of linear antenna arrays.

  18. GLRT Based Anomaly Detection for Sensor Network Monitoring

    KAUST Repository

    Harrou, Fouzi; Sun, Ying

    2015-01-01

    Proper operation of antenna arrays requires continuously monitoring their performances. When a fault occurs in an antenna array, the radiation pattern changes and can significantly deviate from the desired design performance specifications. In this paper, the problem of fault detection in linear antenna arrays is addressed within a statistical framework. Specifically, a statistical fault detection method based on the generalized likelihood ratio (GLR) principle is utilized for detecting potential faults in linear antenna arrays. The proposed method relies on detecting deviations in the radiation pattern of the monitored array with respect to a reference (fault-free) one. To assess the abilities of the GLR based fault detection method, three case studies involving different types of faults have been performed. The simulation results clearly illustrate the effectiveness of the GLR-based fault detection method in monitoring the performance of linear antenna arrays.

  19. A Model-free Approach to Fault Detection of Continuous-time Systems Based on Time Domain Data

    Institute of Scientific and Technical Information of China (English)

    Ping Zhang; Steven X. Ding

    2007-01-01

    In this paper, a model-free approach is presented to design an observer-based fault detection system of linear continuoustime systems based on input and output data in the time domain. The core of the approach is to directly identify parameters of the observer-based residual generator based on a numerically reliable data equation obtained by filtering and sampling the input and output signals.

  20. Software fault detection and recovery in critical real-time systems: An approach based on loose coupling

    International Nuclear Information System (INIS)

    Alho, Pekka; Mattila, Jouni

    2014-01-01

    Highlights: •We analyze fault tolerance in mission-critical real-time systems. •Decoupled architectural model can be used to implement fault tolerance. •Prototype implementation for remote handling control system and service manager. •Recovery from transient faults by restarting services. -- Abstract: Remote handling (RH) systems are used to inspect, make changes to, and maintain components in the ITER machine and as such are an example of mission-critical system. Failure in a critical system may cause damage, significant financial losses and loss of experiment runtime, making dependability one of their most important properties. However, even if the software for RH control systems has been developed using best practices, the system might still fail due to undetected faults (bugs), hardware failures, etc. Critical systems therefore need capability to tolerate faults and resume operation after their occurrence. However, design of effective fault detection and recovery mechanisms poses a challenge due to timeliness requirements, growth in scale, and complex interactions. In this paper we evaluate effectiveness of service-oriented architectural approach to fault tolerance in mission-critical real-time systems. We use a prototype implementation for service management with an experimental RH control system and industrial manipulator. The fault tolerance is based on using the high level of decoupling between services to recover from transient faults by service restarts. In case the recovery process is not successful, the system can still be used if the fault was not in a critical software module

  1. Software fault detection and recovery in critical real-time systems: An approach based on loose coupling

    Energy Technology Data Exchange (ETDEWEB)

    Alho, Pekka, E-mail: pekka.alho@tut.fi; Mattila, Jouni

    2014-10-15

    Highlights: •We analyze fault tolerance in mission-critical real-time systems. •Decoupled architectural model can be used to implement fault tolerance. •Prototype implementation for remote handling control system and service manager. •Recovery from transient faults by restarting services. -- Abstract: Remote handling (RH) systems are used to inspect, make changes to, and maintain components in the ITER machine and as such are an example of mission-critical system. Failure in a critical system may cause damage, significant financial losses and loss of experiment runtime, making dependability one of their most important properties. However, even if the software for RH control systems has been developed using best practices, the system might still fail due to undetected faults (bugs), hardware failures, etc. Critical systems therefore need capability to tolerate faults and resume operation after their occurrence. However, design of effective fault detection and recovery mechanisms poses a challenge due to timeliness requirements, growth in scale, and complex interactions. In this paper we evaluate effectiveness of service-oriented architectural approach to fault tolerance in mission-critical real-time systems. We use a prototype implementation for service management with an experimental RH control system and industrial manipulator. The fault tolerance is based on using the high level of decoupling between services to recover from transient faults by service restarts. In case the recovery process is not successful, the system can still be used if the fault was not in a critical software module.

  2. Weak fault detection and health degradation monitoring using customized standard multiwavelets

    Science.gov (United States)

    Yuan, Jing; Wang, Yu; Peng, Yizhen; Wei, Chenjun

    2017-09-01

    Due to the nonobvious symptoms contaminated by a large amount of background noise, it is challenging to beforehand detect and predictively monitor the weak faults for machinery security assurance. Multiwavelets can act as adaptive non-stationary signal processing tools, potentially viable for weak fault diagnosis. However, the signal-based multiwavelets suffer from such problems as the imperfect properties missing the crucial orthogonality, the decomposition distortion impossibly reflecting the relationships between the faults and signatures, the single objective optimization and independence for fault prognostic. Thus, customized standard multiwavelets are proposed for weak fault detection and health degradation monitoring, especially the weak fault signature quantitative identification. First, the flexible standard multiwavelets are designed using the construction method derived from scalar wavelets, seizing the desired properties for accurate detection of weak faults and avoiding the distortion issue for feature quantitative identification. Second, the multi-objective optimization combined three dimensionless indicators of the normalized energy entropy, normalized singular entropy and kurtosis index is introduced to the evaluation criterions, and benefits for selecting the potential best basis functions for weak faults without the influence of the variable working condition. Third, an ensemble health indicator fused by the kurtosis index, impulse index and clearance index of the original signal along with the normalized energy entropy and normalized singular entropy by the customized standard multiwavelets is achieved using Mahalanobis distance to continuously monitor the health condition and track the performance degradation. Finally, three experimental case studies are implemented to demonstrate the feasibility and effectiveness of the proposed method. The results show that the proposed method can quantitatively identify the fault signature of a slight rub on

  3. Bearing Fault Detection Based on Empirical Wavelet Transform and Correlated Kurtosis by Acoustic Emission.

    Science.gov (United States)

    Gao, Zheyu; Lin, Jing; Wang, Xiufeng; Xu, Xiaoqiang

    2017-05-24

    Rolling bearings are widely used in rotating equipment. Detection of bearing faults is of great importance to guarantee safe operation of mechanical systems. Acoustic emission (AE), as one of the bearing monitoring technologies, is sensitive to weak signals and performs well in detecting incipient faults. Therefore, AE is widely used in monitoring the operating status of rolling bearing. This paper utilizes Empirical Wavelet Transform (EWT) to decompose AE signals into mono-components adaptively followed by calculation of the correlated kurtosis (CK) at certain time intervals of these components. By comparing these CK values, the resonant frequency of the rolling bearing can be determined. Then the fault characteristic frequencies are found by spectrum envelope. Both simulation signal and rolling bearing AE signals are used to verify the effectiveness of the proposed method. The results show that the new method performs well in identifying bearing fault frequency under strong background noise.

  4. Fault Detection and Location of IGBT Short-Circuit Failure in Modular Multilevel Converters

    Directory of Open Access Journals (Sweden)

    Bin Jiang

    2018-06-01

    Full Text Available A single fault detection and location for Modular Multilevel Converter (MMC is of great significance, as numbers of sub-modules (SMs in MMC are connected in series. In this paper, a novel fault detection and location method is proposed for MMC in terms of the Insulated Gate Bipolar Translator (IGBT short-circuit failure in SM. The characteristics of IGBT short-circuit failures are analyzed, based on which a Differential Comparison Low-Voltage Detection Method (DCLVDM is proposed to detect the short-circuit fault. Lastly, the faulty IGBT is located based on the capacitor voltage of the faulty SM by Continuous Wavelet Transform (CWT. Simulations have been done in the simulation software PSCAD/EMTDC and the results confirm the validity and reliability of the proposed method.

  5. Fault detection and diagnosis in asymmetric multilevel inverter using artificial neural network

    Science.gov (United States)

    Raj, Nithin; Jagadanand, G.; George, Saly

    2018-04-01

    The increased component requirement to realise multilevel inverter (MLI) fallout in a higher fault prospect due to power semiconductors. In this scenario, efficient fault detection and diagnosis (FDD) strategies to detect and locate the power semiconductor faults have to be incorporated in addition to the conventional protection systems. Even though a number of FDD methods have been introduced in the symmetrical cascaded H-bridge (CHB) MLIs, very few methods address the FDD in asymmetric CHB-MLIs. In this paper, the gate-open circuit FDD strategy in asymmetric CHB-MLI is presented. Here, a single artificial neural network (ANN) is used to detect and diagnose the fault in both binary and trinary configurations of the asymmetric CHB-MLIs. In this method, features of the output voltage of the MLIs are used as to train the ANN for FDD method. The results prove the validity of the proposed method in detecting and locating the fault in both asymmetric MLI configurations. Finally, the ANN response to the input parameter variation is also analysed to access the performance of the proposed ANN-based FDD strategy.

  6. Fault diagnosis based on controller modification

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik

    2015-01-01

    Detection and isolation of parametric faults in closed-loop systems will be considered in this paper. A major problem is that a feedback controller will in general reduce the effects from variations in the systems including parametric faults on the controlled output from the system. Parametric...... faults can be detected and isolated using active methods, where an auxiliary input is applied. Using active methods for the diagnosis of parametric faults in closed-loop systems, the amplitude of the applied auxiliary input need to be increased to be able to detect and isolate the faults in a reasonable......-parameterization (after Youla, Jabr, Bongiorno and Kucera) for the controller, it is possible to modify the feedback controller with a minor effect on the closed-loop performance in the fault-free case and at the same time optimize the detection and isolation in a faulty case. Controller modification in connection...

  7. Power plant surveillance and fault detection: applications to a commercial PWR

    International Nuclear Information System (INIS)

    Singer, R.M.; Gross, K.C.; Herzog, J.P.; Wegerich, S.; Van Alstine, R.; Bockhorst, F.K.

    1998-01-01

    The theoretical basis and validation studies of a real-time, model-based process monitoring and fault detection system (MSET, multivariate state estimation technique) is presented. Through use of a non-linear state estimation technique coupled with a probabilistically-based statistical hypothesis test, it is possible to detect and identify sensor, component and process faults at extremely early times from changes in the stochastic characteristics of measured signals. Data from an experimental fast reactor and a commercial LWR are used to demonstrate functional capabilities of the monitoring system. In addition, operational data from the Crystal River-3 (CR-3) nuclear power plant are used to illustrate the high sensitivity, accuracy, and the rapid response time of MSET for annunciation of variety signal disturbances. The types of faults detected and identified included the gradual degradation of a venturi flowmeter, rapidly failing flow sensor and the loss-of-time-response of a pressure transmitter. (author)

  8. Control Surface Fault Diagnosis with Specified Detection Probability - Real Event Experiences

    DEFF Research Database (Denmark)

    Hansen, Søren; Blanke, Mogens

    2013-01-01

    desired levels of false alarms and detection probabilities. Self-tuning residual generators are employed for diagnosis and are combined with statistical change detection to form a setup for robust fault diagnosis. On-line estimation of test statistics is used to obtain a detection threshold and a desired...... false alarm probability. A data based method is used to determine the validity of the methods proposed. Verification is achieved using real data and shows that the presented diagnosis method is efficient and could have avoided incidents where faults led to loss of aircraft....

  9. Closed-loop fault detection for full-envelope flight vehicle with measurement delays

    Directory of Open Access Journals (Sweden)

    Wang Zhaolei

    2015-06-01

    Full Text Available A closed-loop fault detection problem is investigated for the full-envelope flight vehicle with measurement delays, where the flight dynamics are modeled as a switched system with delayed feedback signals. The mode-dependent observer-based fault detection filters and state estimation feedback controllers are derived by considering the delays’ impact on the control system and fault detection system simultaneously. Then, considering updating lags of the controllers/filters’ switching signals which are introduced by the delayed measurement of altitude and Mach number, an asynchronous H∞ analysis method is proposed and the system model is further augmented to be an asynchronously switched time-delay system. Also, the global stability and desired performance of the augmented system are guaranteed by combining the switched delay-dependent Lyapunov–Krasovskii functional method with the average dwell time method (ADT, and the delay-dependent existing conditions for the controllers and fault detection filters are obtained in the form of the linear matrix inequalities (LMIs. Finally, numerical example based on the hypersonic vehicles and highly maneuverable technology (HiMAT vehicle is given to demonstrate the merits of the proposed method.

  10. Nonlinear Actuator Fault Detection and Isolation for a VTOL aircraft

    NARCIS (Netherlands)

    De Persis, Claudio; De Santis, Raffaella; Isidori, Alberto

    2001-01-01

    The recently introduced geometric approach to the nonlinear fault detection and isolation problem is used in this paper to detect actuator faults for the vertical takeoff and landing aircraft. The approach leads to a filter which, by processing the outputs of the plant, detects the faults and

  11. Robust fault detection in bond graph framework using interval analysis and Fourier-Motzkin elimination technique

    Science.gov (United States)

    Jha, Mayank Shekhar; Chatti, Nizar; Declerck, Philippe

    2017-09-01

    This paper addresses the fault diagnosis problem of uncertain systems in the context of Bond Graph modelling technique. The main objective is to enhance the fault detection step based on Interval valued Analytical Redundancy Relations (named I-ARR) in order to overcome the problems related to false alarms, missed alarms and robustness issues. These I-ARRs are a set of fault indicators that generate the interval bounds called thresholds. A fault is detected once the nominal residuals (point valued part of I-ARRs) exceed the thresholds. However, the existing fault detection method is limited to parametric faults and it presents various limitations with regards to estimation of measurement signal derivatives, to which I-ARRs are sensitive. The novelties and scientific interest of the proposed methodology are: (1) to improve the accuracy of the measurements derivatives estimation by using a dedicated sliding mode differentiator proposed in this work, (2) to suitably integrate the Fourier-Motzkin Elimination (FME) technique within the I-ARRs based diagnosis so that measurements faults can be detected successfully. The latter provides interval bounds over the derivatives which are included in the thresholds. The proposed methodology is studied under various scenarios (parametric and measurement faults) via simulations over a mechatronic torsion bar system.

  12. Nonlinear Robust Observer-Based Fault Detection for Networked Suspension Control System of Maglev Train

    Directory of Open Access Journals (Sweden)

    Yun Li

    2013-01-01

    Full Text Available A fault detection approach based on nonlinear robust observer is designed for the networked suspension control system of Maglev train with random induced time delay. First, considering random bounded time-delay and external disturbance, the nonlinear model of the networked suspension control system is established. Then, a nonlinear robust observer is designed using the input of the suspension gap. And the estimate error is proved to be bounded with arbitrary precision by adopting an appropriate parameter. When sensor faults happen, the residual between the real states and the observer outputs indicates which kind of sensor failures occurs. Finally, simulation results using the actual parameters of CMS-04 maglev train indicate that the proposed method is effective for maglev train.

  13. Hall Sensor Output Signal Fault-Detection & Safety Implementation Logic

    Directory of Open Access Journals (Sweden)

    Lee SangHun

    2016-01-01

    Full Text Available Recently BLDC motors have been popular in various industrial applications and electric mobility. Recently BLDC motors have been popular in various industrial applications and electric mobility. In most brushless direct current (BLDC motor drives, there are three hall sensors as a position reference. Low resolution hall effect sensor is popularly used to estimate the rotor position because of its good comprehensive performance such as low cost, high reliability and sufficient precision. Various possible faults may happen in a hall effect sensor. This paper presents a fault-tolerant operation method that allows the control of a BLDC motor with one faulty hall sensor and presents the hall sensor output fault-tolerant control strategy. The situations considered are when the output from a hall sensor stays continuously at low or high levels, or a short-time pulse appears on a hall sensor signal. For fault detection, identification of a faulty signal and generating a substitute signal, this method only needs the information from the hall sensors. There are a few research work on hall effect sensor failure of BLDC motor. The conventional fault diagnosis methods are signal analysis, model based analysis and knowledge based analysis. The proposed method is signal based analysis using a compensation signal for reconfiguration and therefore fault diagnosis can be fast. The proposed method is validated to execute the simulation using PSIM.

  14. A Novel Method for Inverter Faults Detection and Diagnosis in PMSM Drives of HEVs based on Discrete Wavelet Transform

    Directory of Open Access Journals (Sweden)

    AKTAS, M.

    2012-11-01

    Full Text Available The paper proposes a novel method, based on wavelet decomposition, for detection and diagnosis of faults (switch short-circuits and switch open-circuits in the driving systems with Field Oriented Controlled Permanent Magnet Synchro?nous Motors (PMSM of Hybrid Electric Vehicles. The fault behaviour of the analyzed system was simulated by Matlab/SIMULINK R2010a. The stator currents during transients were analysed up to the sixth level detail wavelet decomposition by Symlet2 wavelet. The results prove that the proposed fault diagnosis system have very good capabilities.

  15. FSN-based fault modelling for fault detection and troubleshooting in CANDU stations

    Energy Technology Data Exchange (ETDEWEB)

    Nasimi, E., E-mail: elnara.nasimi@brucepower.com [Bruce Power LLP., Tiverton, Ontario(Canada); Gabbar, H.A. [Univ. of Ontario Inst. of Tech., Oshawa, Ontario (Canada)

    2013-07-01

    An accurate fault modeling and troubleshooting methodology is required to aid in making risk-informed decisions related to design and operational activities of current and future generation of CANDU designs. This paper presents fault modeling approach using Fault Semantic Network (FSN) methodology with risk estimation. Its application is demonstrated using a case study of Bruce B zone-control level oscillations. (author)

  16. A Self-Learning Sensor Fault Detection Framework for Industry Monitoring IoT

    Directory of Open Access Journals (Sweden)

    Yu Liu

    2013-01-01

    Full Text Available Many applications based on Internet of Things (IoT technology have recently founded in industry monitoring area. Thousands of sensors with different types work together in an industry monitoring system. Sensors at different locations can generate streaming data, which can be analyzed in the data center. In this paper, we propose a framework for online sensor fault detection. We motivate our technique in the context of the problem of the data value fault detection and event detection. We use the Statistics Sliding Windows (SSW to contain the recent sensor data and regress each window by Gaussian distribution. The regression result can be used to detect the data value fault. Devices on a production line may work in different workloads and the associate sensors will have different status. We divide the sensors into several status groups according to different part of production flow chat. In this way, the status of a sensor is associated with others in the same group. We fit the values in the Status Transform Window (STW to get the slope and generate a group trend vector. By comparing the current trend vector with history ones, we can detect a rational or irrational event. In order to determine parameters for each status group we build a self-learning worker thread in our framework which can edit the corresponding parameter according to the user feedback. Group-based fault detection (GbFD algorithm is proposed in this paper. We test the framework with a simulation dataset extracted from real data of an oil field. Test result shows that GbFD detects 95% sensor fault successfully.

  17. Bayesian fault detection and isolation using Field Kalman Filter

    Science.gov (United States)

    Baranowski, Jerzy; Bania, Piotr; Prasad, Indrajeet; Cong, Tian

    2017-12-01

    Fault detection and isolation is crucial for the efficient operation and safety of any industrial process. There is a variety of methods from all areas of data analysis employed to solve this kind of task, such as Bayesian reasoning and Kalman filter. In this paper, the authors use a discrete Field Kalman Filter (FKF) to detect and recognize faulty conditions in a system. The proposed approach, devised for stochastic linear systems, allows for analysis of faults that can be expressed both as parameter and disturbance variations. This approach is formulated for the situations when the fault catalog is known, resulting in the algorithm allowing estimation of probability values. Additionally, a variant of algorithm with greater numerical robustness is presented, based on computation of logarithmic odds. Proposed algorithm operation is illustrated with numerical examples, and both its merits and limitations are critically discussed and compared with traditional EKF.

  18. GOTRES: an expert system for fault detection and analysis

    International Nuclear Information System (INIS)

    Chung, D.T.; Modarres, M.

    1989-01-01

    This paper describes a deep-knowledge expert system shell for diagnosing faults in process operations. The expert program shell is called GOTRES (GOal TRee Expert System) and uses a goal tree-success tree deep-knowledge structure to model its knowledge-base. To demonstrate GOTRES, we have built an on-line fault diagnosis expert system for an experimental nuclear reactor facility using this shell. The expert system is capable of diagnosing fault conditions using system goal tree as well as utilizing accumulated operating knowledge to predict plant causal and temporal behaviours. The GOTRES shell has also been used for root-cause detection and analysis in a nuclear plant. (author)

  19. Model-Based Fault Diagnosis Techniques Design Schemes, Algorithms and Tools

    CERN Document Server

    Ding, Steven X

    2013-01-01

    Guaranteeing a high system performance over a wide operating range is an important issue surrounding the design of automatic control systems with successively increasing complexity. As a key technology in the search for a solution, advanced fault detection and identification (FDI) is receiving considerable attention. This book introduces basic model-based FDI schemes, advanced analysis and design algorithms, and mathematical and control-theoretic tools. This second edition of Model-Based Fault Diagnosis Techniques contains: ·         new material on fault isolation and identification, and fault detection in feedback control loops; ·         extended and revised treatment of systematic threshold determination for systems with both deterministic unknown inputs and stochastic noises; addition of the continuously-stirred tank heater as a representative process-industrial benchmark; and ·         enhanced discussion of residual evaluation in stochastic processes. Model-based Fault Diagno...

  20. Fault detection of a spur gear using vibration signal with multivariable statistical parameters

    Directory of Open Access Journals (Sweden)

    Songpon Klinchaeam

    2014-10-01

    Full Text Available This paper presents a condition monitoring technique of a spur gear fault detection using vibration signal analysis based on time domain. Vibration signals were acquired from gearboxes and used to simulate various faults on spur gear tooth. In this study, vibration signals were applied to monitor a normal and various fault conditions of a spur gear such as normal, scuffing defect, crack defect and broken tooth. The statistical parameters of vibration signal were used to compare and evaluate the value of fault condition. This technique can be applied to set alarm limit of the signal condition based on statistical parameter such as variance, kurtosis, rms and crest factor. These parameters can be used to set as a boundary decision of signal condition. From the results, the vibration signal analysis with single statistical parameter is unclear to predict fault of the spur gears. The using at least two statistical parameters can be clearly used to separate in every case of fault detection. The boundary decision of statistical parameter with the 99.7% certainty ( 3   from 300 referenced dataset and detected the testing condition with 99.7% ( 3   accuracy and had an error of less than 0.3 % using 50 testing dataset.

  1. Fault detection of the connection of lithium-ion power batteries based on entropy for electric vehicles

    Science.gov (United States)

    Yao, Lei; Wang, Zhenpo; Ma, Jun

    2015-10-01

    This paper proposes a method of fault detection of the connection of Lithium-Ion batteries based on entropy for electric vehicle. In electric vehicle operation process, some factors, such as road conditions, driving habits, vehicle performance, always affect batteries by vibration, which easily cause loosing or virtual connection between batteries. Through the simulation of the battery charging and discharging experiment under vibration environment, the data of voltage fluctuation can be obtained. Meanwhile, an optimal filtering method is adopted using discrete cosine filter method to analyze the characteristics of system noise, based on the voltage set when batteries are working under different vibration frequency. Experimental data processed by filtering is analyzed based on local Shannon entropy, ensemble Shannon entropy and sample entropy. And the best way to find a method of fault detection of the connection of lithium-ion batteries based on entropy is presented for electric vehicle. The experimental data shows that ensemble Shannon entropy can predict the accurate time and the location of battery connection failure in real time. Besides electric-vehicle industry, this method can also be used in other areas in complex vibration environment.

  2. Controller modification applied for active fault detection

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Stoustrup, Jakob; Poulsen, Niels Kjølstad

    2014-01-01

    This paper is focusing on active fault detection (AFD) for parametric faults in closed-loop systems. This auxiliary input applied for the fault detection will also disturb the external output and consequently reduce the performance of the controller. Therefore, only small auxiliary inputs are used...... with the result that the detection and isolation time can be long. In this paper it will be shown, that this problem can be handled by using a modification of the feedback controller. By applying the YJBK-parameterization (after Youla, Jabr, Bongiorno and Kucera) for the controller, it is possible to modify...... the frequency for the auxiliary input is selected. This gives that it is possible to apply an auxiliary input with a reduced amplitude. An example is included to show the results....

  3. Fault detection of sensors in nuclear reactors using self-organizing maps

    Energy Technology Data Exchange (ETDEWEB)

    Barbosa, Paulo Roberto; Tiago, Graziela Marchi [Instituto Federal de Educacao, Ciencia e Tecnologia de Sao Paulo (IFSP), Sao Paulo, SP (Brazil); Bueno, Elaine Inacio [Instituto Federal de Educacao, Ciencia e Tecnologia de Sao Paulo (IFSP), Guarulhos, SP (Brazil); Pereira, Iraci Martinez, E-mail: martinez@ipen.b [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)

    2011-07-01

    In this work a Fault Detection System was developed based on the self-organizing maps methodology. This method was applied to the IEA-R1 research reactor at IPEN using a database generated by a theoretical model of the reactor. The IEA-R1 research reactor is a pool type reactor of 5 MW, cooled and moderated by light water, and uses graphite and beryllium as reflector. The theoretical model was developed using the Matlab Guide toolbox. The equations are based in the IEA-R1 mass and energy inventory balance and physical as well as operational aspects are taken into consideration. In order to test the model ability for fault detection, faults were artificially produced. As the value of the maximum calibration error for special thermocouples is +- 0.5 deg C, it had been inserted faults in the sensor signals with the purpose to produce the database considered in this work. The results show a high percentage of correct classification, encouraging the use of the technique for this type of industrial application. (author)

  4. Fault detection of sensors in nuclear reactors using self-organizing maps

    International Nuclear Information System (INIS)

    Barbosa, Paulo Roberto; Tiago, Graziela Marchi; Bueno, Elaine Inacio; Pereira, Iraci Martinez

    2011-01-01

    In this work a Fault Detection System was developed based on the self-organizing maps methodology. This method was applied to the IEA-R1 research reactor at IPEN using a database generated by a theoretical model of the reactor. The IEA-R1 research reactor is a pool type reactor of 5 MW, cooled and moderated by light water, and uses graphite and beryllium as reflector. The theoretical model was developed using the Matlab Guide toolbox. The equations are based in the IEA-R1 mass and energy inventory balance and physical as well as operational aspects are taken into consideration. In order to test the model ability for fault detection, faults were artificially produced. As the value of the maximum calibration error for special thermocouples is +- 0.5 deg C, it had been inserted faults in the sensor signals with the purpose to produce the database considered in this work. The results show a high percentage of correct classification, encouraging the use of the technique for this type of industrial application. (author)

  5. A method for detection and location of high resistance earth faults

    Energy Technology Data Exchange (ETDEWEB)

    Haenninen, S; Lehtonen, M [VTT Energy, Espoo (Finland); Antila, E [ABB Transmit Oy (Finland)

    1998-08-01

    In the first part of this presentation, the theory of earth faults in unearthed and compensated power systems is briefly presented. The main factors affecting the high resistance fault detection are outlined and common practices for earth fault protection in present systems are summarized. The algorithms of the new method for high resistance fault detection and location are then presented. These are based on the change of neutral voltage and zero sequence currents, measured at the high voltage / medium voltage substation and also at the distribution line locations. The performance of the method is analyzed, and the possible error sources discussed. Among these are, for instance, switching actions, thunder storms and heavy snow fall. The feasibility of the method is then verified by an analysis based both on simulated data, which was derived using an EMTP-ATP simulator, and by real system data recorded during field tests at three substations. For the error source analysis, some real case data recorded during natural power system events, is also used

  6. Fault detection and diagnosis in nonlinear systems a differential and algebraic viewpoint

    CERN Document Server

    Martinez-Guerra, Rafael

    2014-01-01

    The high reliability required in industrial processes has created the necessity of detecting abnormal conditions, called faults, while processes are operating. The term fault generically refers to any type of process degradation, or degradation in equipment performance because of changes in the process's physical characteristics, process inputs or environmental conditions. This book is about the fundamentals of fault detection and diagnosis in a variety of nonlinear systems which are represented by ordinary differential equations. The fault detection problem is approached from a differential algebraic viewpoint, using residual generators based upon high-gain nonlinear auxiliary systems (‘observers’). A prominent role is played by the type of mathematical tools that will be used, requiring knowledge of differential algebra and differential equations. Specific theorems tailored to the needs of the problem-solving procedures are developed and proved. Applications to real-world problems, both with constant an...

  7. Distributed Fault Detection and Isolation for Flocking in a Multi-robot System with Imperfect Communication

    Directory of Open Access Journals (Sweden)

    Shao Shiliang

    2014-06-01

    Full Text Available In this paper, we focus on distributed fault detection and isolation (FDI for a multi-robot system where multiple robots execute a flocking task. Firstly, we propose a fault detection method based on the local-information-exchange and sensor-measurement technologies to cover cases of both perfect communication and imperfect communication. The two detection technologies can be adaptively selected according to the packet loss rate (PLR. Secondly, we design a fault isolation method, considering a situation in which faulty robots still influence the behaviours of other robots. Finally, a complete FDI scheme, based on the proposed detection and isolation methods, is simulated in various scenarios. The results demonstrate that our FDI scheme is effective.

  8. Improved Statistical Fault Detection Technique and Application to Biological Phenomena Modeled by S-Systems.

    Science.gov (United States)

    Mansouri, Majdi; Nounou, Mohamed N; Nounou, Hazem N

    2017-09-01

    In our previous work, we have demonstrated the effectiveness of the linear multiscale principal component analysis (PCA)-based moving window (MW)-generalized likelihood ratio test (GLRT) technique over the classical PCA and multiscale principal component analysis (MSPCA)-based GLRT methods. The developed fault detection algorithm provided optimal properties by maximizing the detection probability for a particular false alarm rate (FAR) with different values of windows, and however, most real systems are nonlinear, which make the linear PCA method not able to tackle the issue of non-linearity to a great extent. Thus, in this paper, first, we apply a nonlinear PCA to obtain an accurate principal component of a set of data and handle a wide range of nonlinearities using the kernel principal component analysis (KPCA) model. The KPCA is among the most popular nonlinear statistical methods. Second, we extend the MW-GLRT technique to one that utilizes exponential weights to residuals in the moving window (instead of equal weightage) as it might be able to further improve fault detection performance by reducing the FAR using exponentially weighed moving average (EWMA). The developed detection method, which is called EWMA-GLRT, provides improved properties, such as smaller missed detection and FARs and smaller average run length. The idea behind the developed EWMA-GLRT is to compute a new GLRT statistic that integrates current and previous data information in a decreasing exponential fashion giving more weight to the more recent data. This provides a more accurate estimation of the GLRT statistic and provides a stronger memory that will enable better decision making with respect to fault detection. Therefore, in this paper, a KPCA-based EWMA-GLRT method is developed and utilized in practice to improve fault detection in biological phenomena modeled by S-systems and to enhance monitoring process mean. The idea behind a KPCA-based EWMA-GLRT fault detection algorithm is to

  9. Fault Detection in Surface PMSM with Applications to Heavy Hybrid Vehicles

    OpenAIRE

    Johnson, Scott; Meyer, Richard T; DeCarlo, Raymond A.; Pekarek, Steve

    2016-01-01

    This report explores detecting inter-turn short circuit (ITSC) faults in surface permanent magnet synchronous machines (SPMSM). ITSC faults are caused by electrical insulation failures in the stator windings and can lead to shorts to ground and even fires. This report proposes methods for detecting these faults using a moving horizon observer (MHO) to reduce the chance of electrical shocks and fires. Specifically, this report constructs a MHO for ITSC fault detection in SPMSM. ITSC fault t...

  10. Sensor Fault Detection and Diagnosis for autonomous vehicles

    Directory of Open Access Journals (Sweden)

    Realpe Miguel

    2015-01-01

    Full Text Available In recent years testing autonomous vehicles on public roads has become a reality. However, before having autonomous vehicles completely accepted on the roads, they have to demonstrate safe operation and reliable interaction with other traffic participants. Furthermore, in real situations and long term operation, there is always the possibility that diverse components may fail. This paper deals with possible sensor faults by defining a federated sensor data fusion architecture. The proposed architecture is designed to detect obstacles in an autonomous vehicle’s environment while detecting a faulty sensor using SVM models for fault detection and diagnosis. Experimental results using sensor information from the KITTI dataset confirm the feasibility of the proposed architecture to detect soft and hard faults from a particular sensor.

  11. Fault tolerant control based on active fault diagnosis

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik

    2005-01-01

    An active fault diagnosis (AFD) method will be considered in this paper in connection with a Fault Tolerant Control (FTC) architecture based on the YJBK parameterization of all stabilizing controllers. The architecture consists of a fault diagnosis (FD) part and a controller reconfiguration (CR......) part. The FTC architecture can be applied for additive faults, parametric faults, and for system structural changes. Only parametric faults will be considered in this paper. The main focus in this paper is on the use of the new approach of active fault diagnosis in connection with FTC. The active fault...... diagnosis approach is based on including an auxiliary input in the system. A fault signature matrix is introduced in connection with AFD, given as the transfer function from the auxiliary input to the residual output. This can be considered as a generalization of the passive fault diagnosis case, where...

  12. Detection of Stator Winding Fault in Induction Motor Using Fuzzy Logic with Optimal Rules

    Directory of Open Access Journals (Sweden)

    Hamid Fekri Azgomi

    2013-04-01

    Full Text Available Induction motors are critical components in many industrial processes. Therefore, swift, precise and reliable monitoring and fault detection systems are required to prevent any further damages. The online monitoring of induction motors has been becoming increasingly important. The main difficulty in this task is the lack of an accurate analytical model to describe a faulty motor. A fuzzy logic approach may help to diagnose traction motor faults. This paper presents a simple method for the detection of stator winding faults (which make up 38% of induction motor failures based on monitoring the line/terminal current amplitudes. In this method, fuzzy logic is used to make decisions about the stator motor condition. In fact, fuzzy logic is reminiscent of human thinking processes and natural language enabling decisions to be made based on vague information. The motor condition is described using linguistic variables. Fuzzy subsets and the corresponding membership functions describe stator current amplitudes. A knowledge base, comprising rule and data bases, is built to support the fuzzy inference. Simulation results are presented to verify the accuracy of motor’s fault detection and knowledge extraction feasibility. The preliminary results show that the proposed fuzzy approach can be used for accurate stator fault diagnosis.

  13. Fault Management: Degradation Signature Detection, Modeling, and Processing, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — Fault to Failure Progression (FFP) signature modeling and processing is a new method for applying condition-based signal data to detect degradation, to identify...

  14. System and method for motor fault detection using stator current noise cancellation

    Science.gov (United States)

    Zhou, Wei; Lu, Bin; Nowak, Michael P.; Dimino, Steven A.

    2010-12-07

    A system and method for detecting incipient mechanical motor faults by way of current noise cancellation is disclosed. The system includes a controller configured to detect indicia of incipient mechanical motor faults. The controller further includes a processor programmed to receive a baseline set of current data from an operating motor and define a noise component in the baseline set of current data. The processor is also programmed to acquire at least on additional set of real-time operating current data from the motor during operation, redefine the noise component present in each additional set of real-time operating current data, and remove the noise component from the operating current data in real-time to isolate any fault components present in the operating current data. The processor is then programmed to generate a fault index for the operating current data based on any isolated fault components.

  15. Adaptive PCA based fault diagnosis scheme in imperial smelting process.

    Science.gov (United States)

    Hu, Zhikun; Chen, Zhiwen; Gui, Weihua; Jiang, Bin

    2014-09-01

    In this paper, an adaptive fault detection scheme based on a recursive principal component analysis (PCA) is proposed to deal with the problem of false alarm due to normal process changes in real process. Our further study is also dedicated to develop a fault isolation approach based on Generalized Likelihood Ratio (GLR) test and Singular Value Decomposition (SVD) which is one of general techniques of PCA, on which the off-set and scaling fault can be easily isolated with explicit off-set fault direction and scaling fault classification. The identification of off-set and scaling fault is also applied. The complete scheme of PCA-based fault diagnosis procedure is proposed. The proposed scheme is first applied to Imperial Smelting Process, and the results show that the proposed strategies can be able to mitigate false alarms and isolate faults efficiently. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

  16. Fault detection of gearbox using time-frequency method

    Science.gov (United States)

    Widodo, A.; Satrijo, Dj.; Prahasto, T.; Haryanto, I.

    2017-04-01

    This research deals with fault detection and diagnosis of gearbox by using vibration signature. In this work, fault detection and diagnosis are approached by employing time-frequency method, and then the results are compared with cepstrum analysis. Experimental work has been conducted for data acquisition of vibration signal thru self-designed gearbox test rig. This test-rig is able to demonstrate normal and faulty gearbox i.e., wears and tooth breakage. Three accelerometers were used for vibration signal acquisition from gearbox, and optical tachometer was used for shaft rotation speed measurement. The results show that frequency domain analysis using fast-fourier transform was less sensitive to wears and tooth breakage condition. However, the method of short-time fourier transform was able to monitor the faults in gearbox. Wavelet Transform (WT) method also showed good performance in gearbox fault detection using vibration signal after employing time synchronous averaging (TSA).

  17. In-flight Fault Detection and Isolation in Aircraft Flight Control Systems

    Science.gov (United States)

    Azam, Mohammad; Pattipati, Krishna; Allanach, Jeffrey; Poll, Scott; Patterson-Hine, Ann

    2005-01-01

    In this paper we consider the problem of test design for real-time fault detection and isolation (FDI) in the flight control system of fixed-wing aircraft. We focus on the faults that are manifested in the control surface elements (e.g., aileron, elevator, rudder and stabilizer) of an aircraft. For demonstration purposes, we restrict our focus on the faults belonging to nine basic fault classes. The diagnostic tests are performed on the features extracted from fifty monitored system parameters. The proposed tests are able to uniquely isolate each of the faults at almost all severity levels. A neural network-based flight control simulator, FLTZ(Registered TradeMark), is used for the simulation of various faults in fixed-wing aircraft flight control systems for the purpose of FDI.

  18. Fault detection in processes represented by PLS models using an EWMA control scheme

    KAUST Repository

    Harrou, Fouzi; Nounou, Mohamed N.; Nounou, Hazem N.

    2016-01-01

    with that of the traditional PLS-based fault detection method through a simulated example involving various fault scenarios that could be encountered in real processes. The simulation results clearly show the effectiveness of the proposed method over the conventional PLS

  19. Integration of control and fault detection

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Stoustrup, J.

    The integrated design of control and fault detection is studied. The result of the analysis is that it is possible to separate the design of the controller and the filter for fault detection in the case where the nominal model can be assumed to be fairly accurate. In the uncertain case, however......, the design of the filter and the controller can not be separated when an optiomal design is desired. For systems with significant uncertainties, there turn out to be a fundamental trade-off between the performance in the control loop and the performance in the filter....

  20. An adaptive and tacholess order analysis method based on enhanced empirical wavelet transform for fault detection of bearings with varying speeds

    Science.gov (United States)

    Hu, Yue; Tu, Xiaotong; Li, Fucai; Li, Hongguang; Meng, Guang

    2017-11-01

    The order tracking method based on time-frequency representation is regarded as an effective tool for fault detection of bearings with varying rotating speeds. In the traditional order tracking methods, a tachometer is required to obtain the instantaneous speed which is hardly satisfied in practice due to the technical and economical limitations. Some tacholess order tracking methods have been developed in recent years. In these methods, the instantaneous frequency ridge extraction is one of the most important parts. However, the current ridge extraction methods are sensitive to noise and may easily get trapped in a local optimum. Due to the presence of noise and other unrelated components of the signal, bearing fault features are difficult to be detected from the envelope spectrum or envelope order spectrum. To overcome the abovementioned drawbacks, an adaptive and tacholess order analysis method is proposed in this paper. In this method, a novel ridge extraction algorithm based on dynamic path optimization is adopted to estimate the instantaneous frequency. This algorithm can overcome the shortcomings of the current ridge extraction algorithms. Meanwhile, the enhanced empirical wavelet transform (EEWT) algorithm is applied to extract the bearing fault features. Both simulated and experimental results demonstrate that the proposed method is robust to noise and effective for bearing fault detection under variable speed conditions.

  1. A Novel Online Data-Driven Algorithm for Detecting UAV Navigation Sensor Faults

    OpenAIRE

    Rui Sun; Qi Cheng; Guanyu Wang; Washington Yotto Ochieng

    2017-01-01

    The use of Unmanned Aerial Vehicles (UAVs) has increased significantly in recent years. On-board integrated navigation sensors are a key component of UAVs’ flight control systems and are essential for flight safety. In order to ensure flight safety, timely and effective navigation sensor fault detection capability is required. In this paper, a novel data-driven Adaptive Neuron Fuzzy Inference System (ANFIS)-based approach is presented for the detection of on-board navigation sensor faults in ...

  2. Model-Based Water Wall Fault Detection and Diagnosis of FBC Boiler Using Strong Tracking Filter

    Directory of Open Access Journals (Sweden)

    Li Sun

    2014-01-01

    Full Text Available Fluidized bed combustion (FBC boilers have received increasing attention in recent decades. The erosion issue on the water wall is one of the most common and serious faults for FBC boilers. Unlike direct measurement of tube thickness used by ultrasonic methods, the wastage of water wall is reconsidered equally as the variation of the overall heat transfer coefficient in the furnace. In this paper, a model-based approach is presented to estimate internal states and heat transfer coefficient dually from the noisy measurable outputs. The estimated parameter is compared with the normal value. Then the modified Bayesian algorithm is adopted for fault detection and diagnosis (FDD. The simulation results demonstrate that the approach is feasible and effective.

  3. A Fault Detection Filtering for Networked Control Systems Based on Balanced Reduced-Order

    Directory of Open Access Journals (Sweden)

    Da-Meng Dai

    2015-01-01

    Full Text Available Due to the probability of the packet dropout in the networked control systems, a balanced reduced-order fault detection filter is proposed. In this paper, we first analyze the packet dropout effects in the networked control systems. Then, in order to obtain a robust fault detector for the packet dropout, we use the balanced structure to construct a reduced-order model for residual dynamics. Simulation results are provided to testify the proposed method.

  4. Safety assessment of automated vehicle functions by simulation-based fault injection

    OpenAIRE

    Juez, Garazi; Amparan, Estibaliz; Lattarulo, Ray; Rastelli, Joshue Perez; Ruiz, Alejandra; Espinoza, Huascar

    2017-01-01

    As automated driving vehicles become more sophisticated and pervasive, it is increasingly important to assure its safety even in the presence of faults. This paper presents a simulation-based fault injection approach (Sabotage) aimed at assessing the safety of automated vehicle functions. In particular, we focus on a case study to forecast fault effects during the model-based design of a lateral control function. The goal is to determine the acceptable fault detection interval for pe...

  5. Fault Detection and Load Distribution for the Wind Farm Challenge

    DEFF Research Database (Denmark)

    Borchersen, Anders Bech; Larsen, Jesper Abildgaard; Stoustrup, Jakob

    2014-01-01

    In this paper a fault detection system and a fault tolerant controller for a wind farm model is designed and tested. The wind farm model is taken from the wind farm challenge which is a public available challenge where a wind farm consisting of nine turbines is proposed. The goal of the challenge...... normal and faulty conditions. Thus a fault detection system and a fault tolerant controller has been designed and combined. The fault tolerant control system has then been tested and compared to the reference system and shows improvement on all measures....

  6. Fault tolerant system based on IDDQ testing

    Science.gov (United States)

    Guibane, Badi; Hamdi, Belgacem; Mtibaa, Abdellatif; Bensalem, Brahim

    2018-06-01

    Offline test is essential to ensure good manufacturing quality. However, for permanent or transient faults that occur during the use of the integrated circuit in an application, an online integrated test is needed as well. This procedure should ensure the detection and possibly the correction or the masking of these faults. This requirement of self-correction is sometimes necessary, especially in critical applications that require high security such as automotive, space or biomedical applications. We propose a fault-tolerant design for analogue and mixed-signal design complementary metal oxide (CMOS) circuits based on the quiescent current supply (IDDQ) testing. A defect can cause an increase in current consumption. IDDQ testing technique is based on the measurement of power supply current to distinguish between functional and failed circuits. The technique has been an effective testing method for detecting physical defects such as gate-oxide shorts, floating gates (open) and bridging defects in CMOS integrated circuits. An architecture called BICS (Built In Current Sensor) is used for monitoring the supply current (IDDQ) of the connected integrated circuit. If the measured current is not within the normal range, a defect is signalled and the system switches connection from the defective to a functional integrated circuit. The fault-tolerant technique is composed essentially by a double mirror built-in current sensor, allowing the detection of abnormal current consumption and blocks allowing the connection to redundant circuits, if a defect occurs. Spices simulations are performed to valid the proposed design.

  7. Detection of Inter-turn Faults in Five-Phase Permanent Magnet Synchronous Motors

    Directory of Open Access Journals (Sweden)

    SAAVEDRA, H.

    2014-11-01

    Full Text Available Five-phase permanent magnet synchronous motors (PMSMs have inherent fault-tolerant capabilities. This paper analyzes the detection of inter-turn short circuit faults in five-phase PMSMs in their early stage, i.e. with only one turn in short circuit by means of the analysis of the stator currents and the zero-sequence voltage component (ZSVC spectra. For this purpose, a parametric model of five-phase PMSMs which accounts for the effects of inter-turn short circuits is developed to determine the most suitable harmonic frequencies to be analyzed to detect such faults. The amplitudes of these fault harmonic are analyzed in detail by means of finite-elements method (FEM simulations, which corroborate the predictions of the parametric model. A low-speed five-phase PMSM for in-wheel applications is studied and modeled. This paper shows that the ZSVC-based method provides better sensitivity to diagnose inter-turn faults in the analyzed low-speed application. Results presented under a wide speed range and different load levels show that it is feasible to diagnose such faults in their early stage, thus allowing applying a post-fault strategy to minimize their effects while ensuring a safe operation.

  8. Application of D-S Evidence Fusion Method in the Fault Detection of Temperature Sensor

    Directory of Open Access Journals (Sweden)

    Zheng Dou

    2014-01-01

    Full Text Available Due to the complexity and dangerousness of drying process, the fault detection of temperature sensor is very difficult and dangerous in actual working practice and the detection effectiveness is not satisfying. For this problem, in this paper, based on the idea of information fusion and the requirements of D-S evidence method, a D-S evidence fusion structure with two layers was introduced to detect the temperature sensor fault in drying process. The first layer was data layer to establish the basic belief assignment function of evidence which could be realized by BP Neural Network. The second layer was decision layer to detect and locate the sensor fault which could be realized by D-S evidence fusion method. According to the numerical simulation results, the working conditions of sensors could be described effectively and accurately by this method, so that it could be used to detect and locate the sensor fault.

  9. Fault tolerant control for uncertain systems with parametric faults

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2006-01-01

    A fault tolerant control (FTC) architecture based on active fault diagnosis (AFD) and the YJBK (Youla, Jarb, Bongiorno and Kucera)parameterization is applied in this paper. Based on the FTC architecture, fault tolerant control of uncertain systems with slowly varying parametric faults...... is investigated. Conditions are given for closed-loop stability in case of false alarms or missing fault detection/isolation....

  10. Development of a morphological convolution operator for bearing fault detection

    Science.gov (United States)

    Li, Yifan; Liang, Xihui; Liu, Weiwei; Wang, Yan

    2018-05-01

    This paper presents a novel signal processing scheme, namely morphological convolution operator (MCO) lifted morphological undecimated wavelet (MUDW), for rolling element bearing fault detection. In this scheme, a MCO is first designed to fully utilize the advantage of the closing & opening gradient operator and the closing-opening & opening-closing gradient operator for feature extraction as well as the merit of excellent denoising characteristics of the convolution operator. The MCO is then introduced into MUDW for the purpose of improving the fault detection ability of the reported MUDWs. Experimental vibration signals collected from a train wheelset test rig and the bearing data center of Case Western Reserve University are employed to evaluate the effectiveness of the proposed MCO lifted MUDW on fault detection of rolling element bearings. The results show that the proposed approach has a superior performance in extracting fault features of defective rolling element bearings. In addition, comparisons are performed between two reported MUDWs and the proposed MCO lifted MUDW. The MCO lifted MUDW outperforms both of them in detection of outer race faults and inner race faults of rolling element bearings.

  11. Optimization of Second Fault Detection Thresholds to Maximize Mission POS

    Science.gov (United States)

    Anzalone, Evan

    2018-01-01

    In order to support manned spaceflight safety requirements, the Space Launch System (SLS) has defined program-level requirements for key systems to ensure successful operation under single fault conditions. To accommodate this with regards to Navigation, the SLS utilizes an internally redundant Inertial Navigation System (INS) with built-in capability to detect, isolate, and recover from first failure conditions and still maintain adherence to performance requirements. The unit utilizes multiple hardware- and software-level techniques to enable detection, isolation, and recovery from these events in terms of its built-in Fault Detection, Isolation, and Recovery (FDIR) algorithms. Successful operation is defined in terms of sufficient navigation accuracy at insertion while operating under worst case single sensor outages (gyroscope and accelerometer faults at launch). In addition to first fault detection and recovery, the SLS program has also levied requirements relating to the capability of the INS to detect a second fault, tracking any unacceptable uncertainty in knowledge of the vehicle's state. This detection functionality is required in order to feed abort analysis and ensure crew safety. Increases in navigation state error and sensor faults can drive the vehicle outside of its operational as-designed environments and outside of its performance envelope causing loss of mission, or worse, loss of crew. The criteria for operation under second faults allows for a larger set of achievable missions in terms of potential fault conditions, due to the INS operating at the edge of its capability. As this performance is defined and controlled at the vehicle level, it allows for the use of system level margins to increase probability of mission success on the operational edges of the design space. Due to the implications of the vehicle response to abort conditions (such as a potentially failed INS), it is important to consider a wide range of failure scenarios in terms of

  12. Fault detection in multiply-redundant measurement systems via sequential testing

    International Nuclear Information System (INIS)

    Ray, A.

    1988-01-01

    The theory and application of a sequential test procedure for fault detection and isolation. The test procedure is suited for development of intelligent instrumentation in strategic processes like aircraft and nuclear plants where redundant measurements are usually available for individual critical variables. The test procedure consists of: (1) a generic redundancy management procedure which is essentially independent of the fault detection strategy and measurement noise statistics, and (2) a modified version of sequential probability ratio test algorithm for fault detection and isolation, which functions within the framework of this redundancy management procedure. The sequential test procedure is suitable for real-time applications using commercially available microcomputers and its efficacy has been verified by online fault detection in an operating nuclear reactor. 15 references

  13. Transient pattern analysis for fault detection and diagnosis of HVAC systems

    International Nuclear Information System (INIS)

    Cho, Sung-Hwan; Yang, Hoon-Cheol; Zaheer-uddin, M.; Ahn, Byung-Cheon

    2005-01-01

    Modern building HVAC systems are complex and consist of a large number of interconnected sub-systems and components. In the event of a fault, it becomes very difficult for the operator to locate and isolate the faulty component in such large systems using conventional fault detection methods. In this study, transient pattern analysis is explored as a tool for fault detection and diagnosis of an HVAC system. Several tests involving different fault replications were conducted in an environmental chamber test facility. The results show that the evolution of fault residuals forms clear and distinct patterns that can be used to isolate faults. It was found that the time needed to reach steady state for a typical building HVAC system is at least 50-60 min. This means incorrect diagnosis of faults can happen during online monitoring if the transient pattern responses are not considered in the fault detection and diagnosis analysis

  14. A Survey on Distributed Filtering and Fault Detection for Sensor Networks

    Directory of Open Access Journals (Sweden)

    Hongli Dong

    2014-01-01

    Full Text Available In recent years, theoretical and practical research on large-scale networked systems has gained an increasing attention from multiple disciplines including engineering, computer science, and mathematics. Lying in the core part of the area are the distributed estimation and fault detection problems that have recently been attracting growing research interests. In particular, an urgent need has arisen to understand the effects of distributed information structures on filtering and fault detection in sensor networks. In this paper, a bibliographical review is provided on distributed filtering and fault detection problems over sensor networks. The algorithms employed to study the distributed filtering and detection problems are categorised and then discussed. In addition, some recent advances on distributed detection problems for faulty sensors and fault events are also summarized in great detail. Finally, we conclude the paper by outlining future research challenges for distributed filtering and fault detection for sensor networks.

  15. All-to-all sequenced fault detection system

    Science.gov (United States)

    Archer, Charles Jens; Pinnow, Kurt Walter; Ratterman, Joseph D.; Smith, Brian Edward

    2010-11-02

    An apparatus, program product and method enable nodal fault detection by sequencing communications between all system nodes. A master node may coordinate communications between two slave nodes before sequencing to and initiating communications between a new pair of slave nodes. The communications may be analyzed to determine the nodal fault.

  16. Soft-Fault Detection Technologies Developed for Electrical Power Systems

    Science.gov (United States)

    Button, Robert M.

    2004-01-01

    The NASA Glenn Research Center, partner universities, and defense contractors are working to develop intelligent power management and distribution (PMAD) technologies for future spacecraft and launch vehicles. The goals are to provide higher performance (efficiency, transient response, and stability), higher fault tolerance, and higher reliability through the application of digital control and communication technologies. It is also expected that these technologies will eventually reduce the design, development, manufacturing, and integration costs for large, electrical power systems for space vehicles. The main focus of this research has been to incorporate digital control, communications, and intelligent algorithms into power electronic devices such as direct-current to direct-current (dc-dc) converters and protective switchgear. These technologies, in turn, will enable revolutionary changes in the way electrical power systems are designed, developed, configured, and integrated in aerospace vehicles and satellites. Initial successes in integrating modern, digital controllers have proven that transient response performance can be improved using advanced nonlinear control algorithms. One technology being developed includes the detection of "soft faults," those not typically covered by current systems in use today. Soft faults include arcing faults, corona discharge faults, and undetected leakage currents. Using digital control and advanced signal analysis algorithms, we have shown that it is possible to reliably detect arcing faults in high-voltage dc power distribution systems (see the preceding photograph). Another research effort has shown that low-level leakage faults and cable degradation can be detected by analyzing power system parameters over time. This additional fault detection capability will result in higher reliability for long-lived power systems such as reusable launch vehicles and space exploration missions.

  17. Method and system for early detection of incipient faults in electric motors

    Science.gov (United States)

    Parlos, Alexander G; Kim, Kyusung

    2003-07-08

    A method and system for early detection of incipient faults in an electric motor are disclosed. First, current and voltage values for one or more phases of the electric motor are measured during motor operations. A set of current predictions is then determined via a neural network-based current predictor based on the measured voltage values and an estimate of motor speed values of the electric motor. Next, a set of residuals is generated by combining the set of current predictions with the measured current values. A set of fault indicators is subsequently computed from the set of residuals and the measured current values. Finally, a determination is made as to whether or not there is an incipient electrical, mechanical, and/or electromechanical fault occurring based on the comparison result of the set of fault indicators and a set of predetermined baseline values.

  18. Model based fault diagnosis in a centrifugal pump application using structural analysis

    DEFF Research Database (Denmark)

    Kallesøe, C. S.; Izadi-Zamanabadi, Roozbeh; Rasmussen, Henrik

    2004-01-01

    A model based approach for fault detection and isolation in a centrifugal pump is proposed in this paper. The fault detection algorithm is derived using a combination of structural analysis, Analytical Redundant Relations (ARR) and observer designs. Structural considerations on the system are used...

  19. Model Based Fault Diagnosis in a Centrifugal Pump Application using Structural Analysis

    DEFF Research Database (Denmark)

    Kallesøe, C. S.; Izadi-Zamanabadi, Roozbeh; Rasmussen, Henrik

    2004-01-01

    A model based approach for fault detection and isolation in a centrifugal pump is proposed in this paper. The fault detection algorithm is derived using a combination of structural analysis, Analytical Redundant Relations (ARR) and observer designs. Structural considerations on the system are used...

  20. Fundamental problems in fault detection and identification

    DEFF Research Database (Denmark)

    Saberi, Ali; Stoorvogel, Anton A.; Sannuti, Peddapullaiah

    1999-01-01

    For certain fundamental problems in fault detection and identification, the necessary and sufficient conditions for their solvability are derived. These conditions are weaker than the ones found in the literature, since we do not assume any particular structure for the residual generator......For certain fundamental problems in fault detection and identification, the necessary and sufficient conditions for their solvability are derived. These conditions are weaker than the ones found in the literature, since we do not assume any particular structure for the residual generator...

  1. Multiscale Permutation Entropy Based Rolling Bearing Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Jinde Zheng

    2014-01-01

    Full Text Available A new rolling bearing fault diagnosis approach based on multiscale permutation entropy (MPE, Laplacian score (LS, and support vector machines (SVMs is proposed in this paper. Permutation entropy (PE was recently proposed and defined to measure the randomicity and detect dynamical changes of time series. However, for the complexity of mechanical systems, the randomicity and dynamic changes of the vibration signal will exist in different scales. Thus, the definition of MPE is introduced and employed to extract the nonlinear fault characteristics from the bearing vibration signal in different scales. Besides, the SVM is utilized to accomplish the fault feature classification to fulfill diagnostic procedure automatically. Meanwhile, in order to avoid a high dimension of features, the Laplacian score (LS is used to refine the feature vector by ranking the features according to their importance and correlations with the main fault information. Finally, the rolling bearing fault diagnosis method based on MPE, LS, and SVM is proposed and applied to the experimental data. The experimental data analysis results indicate that the proposed method could identify the fault categories effectively.

  2. Shallow Faulting in Morelia, Mexico, Based on Seismic Tomography and Geodetically Detected Land Subsidence

    Science.gov (United States)

    Cabral-Cano, E.; Arciniega-Ceballos, A.; Vergara-Huerta, F.; Chaussard, E.; Wdowinski, S.; DeMets, C.; Salazar-Tlaczani, L.

    2013-12-01

    Subsidence has been a common occurrence in several cities in central Mexico for the past three decades. This process causes substantial damage to the urban infrastructure and housing in several cities and it is a major factor to be considered when planning urban development, land-use zoning and hazard mitigation strategies. Since the early 1980's the city of Morelia in Central Mexico has experienced subsidence associated with groundwater extraction in excess of natural recharge from rainfall. Previous works have focused on the detection and temporal evolution of the subsidence spatial distribution. The most recent InSAR analysis confirms the permanence of previously detected rapidly subsiding areas such as the Rio Grande Meander area and also defines 2 subsidence patches previously undetected in the newly developed suburban sectors west of Morelia at the Fraccionamiento Del Bosque along, south of Hwy. 15 and another patch located north of Morelia along Gabino Castañeda del Rio Ave. Because subsidence-induced, shallow faulting develops at high horizontal strain localization, newly developed a subsidence areas are particularly prone to faulting and fissuring. Shallow faulting increases groundwater vulnerability because it disrupts discharge hydraulic infrastructure and creates a direct path for transport of surface pollutants into the underlying aquifer. Other sectors in Morelia that have been experiencing subsidence for longer time have already developed well defined faults such as La Colina, Central Camionera, Torremolinos and La Paloma faults. Local construction codes in the vicinity of these faults define a very narrow swath along which housing construction is not allowed. In order to better characterize these fault systems and provide better criteria for future municipal construction codes we have surveyed the La Colina and Torremolinos fault systems in the western sector of Morelia using seismic tomographic techniques. Our results indicate that La Colina Fault

  3. Fault detection and classification in electrical power transmission system using artificial neural network.

    Science.gov (United States)

    Jamil, Majid; Sharma, Sanjeev Kumar; Singh, Rajveer

    2015-01-01

    This paper focuses on the detection and classification of the faults on electrical power transmission line using artificial neural networks. The three phase currents and voltages of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. A detailed analysis with varying number of hidden layers has been performed to validate the choice of the neural network. The simulation results concluded that the present method based on the neural network is efficient in detecting and classifying the faults on transmission lines with satisfactory performances. The different faults are simulated with different parameters to check the versatility of the method. The proposed method can be extended to the Distribution network of the Power System. The various simulations and analysis of signals is done in the MATLAB(®) environment.

  4. A first approach on fault detection and isolation for cardiovascular anomalies detection

    KAUST Repository

    Ledezma, Fernando

    2015-07-01

    In this paper, we use an extended version of the cardiovascular system\\'s state space model presented by [1] and propose a fault detection and isolation methodology to study the problem of detecting cardiovascular anomalies that can originate from variations in physiological parameters and deviations in the performance of the heart\\'s mitral and aortic valves. An observer-based approach is discussed as the basis of the method. The approach contemplates a bank of Extended Kalman Filters to achieve joint estimation of the model\\'s states and parameters and to detect malfunctions in the valves\\' performance. © 2015 American Automatic Control Council.

  5. Fault detection by surface seismic scanning tunneling macroscope: Field test

    KAUST Repository

    Hanafy, Sherif M.

    2014-08-05

    The seismic scanning tunneling macroscope (SSTM) is proposed for detecting the presence of near-surface impedance anomalies and faults. Results with synthetic data are consistent with theory in that scatterers closer to the surface provide brighter SSTM profiles than those that are deeper. The SSTM profiles show superresolution detection if the scatterers are in the near-field region of the recording line. The field data tests near Gulf of Aqaba, Haql, KSA clearly show the presence of the observable fault scarp, and identify the subsurface presence of the hidden faults indicated in the tomograms. Superresolution detection of the fault is achieved, even when the 35 Hz data are lowpass filtered to the 5-10 Hz band.

  6. Fault detection by surface seismic scanning tunneling macroscope: Field test

    KAUST Repository

    Hanafy, Sherif M.; Schuster, Gerard T.

    2014-01-01

    The seismic scanning tunneling macroscope (SSTM) is proposed for detecting the presence of near-surface impedance anomalies and faults. Results with synthetic data are consistent with theory in that scatterers closer to the surface provide brighter SSTM profiles than those that are deeper. The SSTM profiles show superresolution detection if the scatterers are in the near-field region of the recording line. The field data tests near Gulf of Aqaba, Haql, KSA clearly show the presence of the observable fault scarp, and identify the subsurface presence of the hidden faults indicated in the tomograms. Superresolution detection of the fault is achieved, even when the 35 Hz data are lowpass filtered to the 5-10 Hz band.

  7. Smart Sensor for Online Detection of Multiple-Combined Faults in VSD-Fed Induction Motors

    Science.gov (United States)

    Garcia-Ramirez, Armando G.; Osornio-Rios, Roque A.; Granados-Lieberman, David; Garcia-Perez, Arturo; Romero-Troncoso, Rene J.

    2012-01-01

    Induction motors fed through variable speed drives (VSD) are widely used in different industrial processes. Nowadays, the industry demands the integration of smart sensors to improve the fault detection in order to reduce cost, maintenance and power consumption. Induction motors can develop one or more faults at the same time that can be produce severe damages. The combined fault identification in induction motors is a demanding task, but it has been rarely considered in spite of being a common situation, because it is difficult to identify two or more faults simultaneously. This work presents a smart sensor for online detection of simple and multiple-combined faults in induction motors fed through a VSD in a wide frequency range covering low frequencies from 3 Hz and high frequencies up to 60 Hz based on a primary sensor being a commercially available current clamp or a hall-effect sensor. The proposed smart sensor implements a methodology based on the fast Fourier transform (FFT), RMS calculation and artificial neural networks (ANN), which are processed online using digital hardware signal processing based on field programmable gate array (FPGA).

  8. Bond graph model-based fault diagnosis of hybrid systems

    CERN Document Server

    Borutzky, Wolfgang

    2015-01-01

    This book presents a bond graph model-based approach to fault diagnosis in mechatronic systems appropriately represented by a hybrid model. The book begins by giving a survey of the fundamentals of fault diagnosis and failure prognosis, then recalls state-of-art developments referring to latest publications, and goes on to discuss various bond graph representations of hybrid system models, equations formulation for switched systems, and simulation of their dynamic behavior. The structured text: • focuses on bond graph model-based fault detection and isolation in hybrid systems; • addresses isolation of multiple parametric faults in hybrid systems; • considers system mode identification; • provides a number of elaborated case studies that consider fault scenarios for switched power electronic systems commonly used in a variety of applications; and • indicates that bond graph modelling can also be used for failure prognosis. In order to facilitate the understanding of fault diagnosis and the presented...

  9. Double Fault Detection of Cone-Shaped Redundant IMUs Using Wavelet Transformation and EPSA

    Directory of Open Access Journals (Sweden)

    Wonhee Lee

    2014-02-01

    Full Text Available A model-free hybrid fault diagnosis technique is proposed to improve the performance of single and double fault detection and isolation. This is a model-free hybrid method which combines the extended parity space approach (EPSA with a multi-resolution signal decomposition by using a discrete wavelet transform (DWT. Conventional EPSA can detect and isolate single and double faults. The performance of fault detection and isolation is influenced by the relative size of noise and fault. In this paper; the DWT helps to cancel the high frequency sensor noise. The proposed technique can improve low fault detection and isolation probability by utilizing the EPSA with DWT. To verify the effectiveness of the proposed fault detection method Monte Carlo numerical simulations are performed for a redundant inertial measurement unit (RIMU.

  10. Double Fault Detection of Cone-Shaped Redundant IMUs Using Wavelet Transformation and EPSA

    Science.gov (United States)

    Lee, Wonhee; Park, Chan Gook

    2014-01-01

    A model-free hybrid fault diagnosis technique is proposed to improve the performance of single and double fault detection and isolation. This is a model-free hybrid method which combines the extended parity space approach (EPSA) with a multi-resolution signal decomposition by using a discrete wavelet transform (DWT). Conventional EPSA can detect and isolate single and double faults. The performance of fault detection and isolation is influenced by the relative size of noise and fault. In this paper; the DWT helps to cancel the high frequency sensor noise. The proposed technique can improve low fault detection and isolation probability by utilizing the EPSA with DWT. To verify the effectiveness of the proposed fault detection method Monte Carlo numerical simulations are performed for a redundant inertial measurement unit (RIMU). PMID:24556675

  11. Early detection of incipient faults in power plants using accelerated neural network learning

    International Nuclear Information System (INIS)

    Parlos, A.G.; Jayakumar, M.; Atiya, A.

    1992-01-01

    An important aspect of power plant automation is the development of computer systems able to detect and isolate incipient (slowly developing) faults at the earliest possible stages of their occurrence. In this paper, the development and testing of such a fault detection scheme is presented based on recognition of sensor signatures during various failure modes. An accelerated learning algorithm, namely adaptive backpropagation (ABP), has been developed that allows the training of a multilayer perceptron (MLP) network to a high degree of accuracy, with an order of magnitude improvement in convergence speed. An artificial neural network (ANN) has been successfully trained using the ABP algorithm, and it has been extensively tested with simulated data to detect and classify incipient faults of various types and severity and in the presence of varying sensor noise levels

  12. A Novel Approach to Detect Faults Occurring During Power Swings by Abrupt Change of Impedance Trajectory

    DEFF Research Database (Denmark)

    Khodaparast, Jalal; Khederzadeh, M.; Silva, Filipe Miguel Faria da

    2017-01-01

    The main purpose of power swing blocking is to distinguish faults from power swings. However, faults occurred during a power swing should still be detected and cleared promptly. This paper proposes an index based on detecting abrupt jump of impedance trajectory by utilization of the predicting...... of Taylor expansion is used to decrease the corrugation effect of impedance estimation and increase the reliability of the proposed method. Furthermore, in order to increase the selectivity of the proposed method, the proposed index is armed with phase comparison logic to detect internal faults...

  13. Power plant fault detection using artificial neural network

    Science.gov (United States)

    Thanakodi, Suresh; Nazar, Nazatul Shiema Moh; Joini, Nur Fazriana; Hidzir, Hidzrin Dayana Mohd; Awira, Mohammad Zulfikar Khairul

    2018-02-01

    The fault that commonly occurs in power plants is due to various factors that affect the system outage. There are many types of faults in power plants such as single line to ground fault, double line to ground fault, and line to line fault. The primary aim of this paper is to diagnose the fault in 14 buses power plants by using an Artificial Neural Network (ANN). The Multilayered Perceptron Network (MLP) that detection trained utilized the offline training methods such as Gradient Descent Backpropagation (GDBP), Levenberg-Marquardt (LM), and Bayesian Regularization (BR). The best method is used to build the Graphical User Interface (GUI). The modelling of 14 buses power plant, network training, and GUI used the MATLAB software.

  14. Aircraft applications of fault detection and isolation techniques

    Science.gov (United States)

    Marcos Esteban, Andres

    In this thesis the problems of fault detection & isolation and fault tolerant systems are studied from the perspective of LTI frequency-domain, model-based techniques. Emphasis is placed on the applicability of these LTI techniques to nonlinear models, especially to aerospace systems. Two applications of Hinfinity LTI fault diagnosis are given using an open-loop (no controller) design approach: one for the longitudinal motion of a Boeing 747-100/200 aircraft, the other for a turbofan jet engine. An algorithm formalizing a robust identification approach based on model validation ideas is also given and applied to the previous jet engine. A general linear fractional transformation formulation is given in terms of the Youla and Dual Youla parameterizations for the integrated (control and diagnosis filter) approach. This formulation provides better insight into the trade-off between the control and the diagnosis objectives. It also provides the basic groundwork towards the development of nested schemes for the integrated approach. These nested structures allow iterative improvements on the control/filter Youla parameters based on successive identification of the system uncertainty (as given by the Dual Youla parameter). The thesis concludes with an application of Hinfinity LTI techniques to the integrated design for the longitudinal motion of the previous Boeing 747-100/200 model.

  15. A system for the non-destructive detection of faults in safety fuses using radioisotopes

    International Nuclear Information System (INIS)

    Goncalves, D.

    1980-01-01

    Design of an equipment for on line detection of faults in the safety fuses for conventional explosives employing transmission of #betta#-radiation is reported. The faults are detected by an ion-chamber based on the variation of the intensity of the beta particles transmitted through the fuse during its passage across the collimated beam. Strontium-90 encapsulated in stainless steel or aluminum is used as the #betta#-source. An electrical signal corresponding the fault is obtained by subtraction of an external current, that is equivalent to the output of the ion-chamber in the presence of faultless fuse. (Author) [pt

  16. Reliable fault detection and diagnosis of photovoltaic systems based on statistical monitoring approaches

    KAUST Repository

    Harrou, Fouzi; Sun, Ying; Taghezouit, Bilal; Saidi, Ahmed; Hamlati, Mohamed-Elkarim

    2017-01-01

    This study reports the development of an innovative fault detection and diagnosis scheme to monitor the direct current (DC) side of photovoltaic (PV) systems. Towards this end, we propose a statistical approach that exploits the advantages of one

  17. Nonlinear Process Fault Diagnosis Based on Serial Principal Component Analysis.

    Science.gov (United States)

    Deng, Xiaogang; Tian, Xuemin; Chen, Sheng; Harris, Chris J

    2018-03-01

    Many industrial processes contain both linear and nonlinear parts, and kernel principal component analysis (KPCA), widely used in nonlinear process monitoring, may not offer the most effective means for dealing with these nonlinear processes. This paper proposes a new hybrid linear-nonlinear statistical modeling approach for nonlinear process monitoring by closely integrating linear principal component analysis (PCA) and nonlinear KPCA using a serial model structure, which we refer to as serial PCA (SPCA). Specifically, PCA is first applied to extract PCs as linear features, and to decompose the data into the PC subspace and residual subspace (RS). Then, KPCA is performed in the RS to extract the nonlinear PCs as nonlinear features. Two monitoring statistics are constructed for fault detection, based on both the linear and nonlinear features extracted by the proposed SPCA. To effectively perform fault identification after a fault is detected, an SPCA similarity factor method is built for fault recognition, which fuses both the linear and nonlinear features. Unlike PCA and KPCA, the proposed method takes into account both linear and nonlinear PCs simultaneously, and therefore, it can better exploit the underlying process's structure to enhance fault diagnosis performance. Two case studies involving a simulated nonlinear process and the benchmark Tennessee Eastman process demonstrate that the proposed SPCA approach is more effective than the existing state-of-the-art approach based on KPCA alone, in terms of nonlinear process fault detection and identification.

  18. Use of Sparse Principal Component Analysis (SPCA) for Fault Detection

    DEFF Research Database (Denmark)

    Gajjar, Shriram; Kulahci, Murat; Palazoglu, Ahmet

    2016-01-01

    Principal component analysis (PCA) has been widely used for data dimension reduction and process fault detection. However, interpreting the principal components and the outcomes of PCA-based monitoring techniques is a challenging task since each principal component is a linear combination of the ...

  19. STEM - software test and evaluation methods: fault detection using static analysis techniques

    International Nuclear Information System (INIS)

    Bishop, P.G.; Esp, D.G.

    1988-08-01

    STEM is a software reliability project with the objective of evaluating a number of fault detection and fault estimation methods which can be applied to high integrity software. This Report gives some interim results of applying both manual and computer-based static analysis techniques, in particular SPADE, to an early CERL version of the PODS software containing known faults. The main results of this study are that: The scope for thorough verification is determined by the quality of the design documentation; documentation defects become especially apparent when verification is attempted. For well-defined software, the thoroughness of SPADE-assisted verification for detecting a large class of faults was successfully demonstrated. For imprecisely-defined software (not recommended for high-integrity systems) the use of tools such as SPADE is difficult and inappropriate. Analysis and verification tools are helpful, through their reliability and thoroughness. However, they are designed to assist, not replace, a human in validating software. Manual inspection can still reveal errors (such as errors in specification and errors of transcription of systems constants) which current tools cannot detect. There is a need for tools to automatically detect typographical errors in system constants, for example by reporting outliers to patterns. To obtain the maximum benefit from advanced tools, they should be applied during software development (when verification problems can be detected and corrected) rather than retrospectively. (author)

  20. Application of a Fault Detection and Isolation System on a Rotary Machine

    Directory of Open Access Journals (Sweden)

    Silvia M. Zanoli

    2013-01-01

    Full Text Available The paper illustrates the design and the implementation of a Fault Detection and Isolation (FDI system to a rotary machine like a multishaft centrifugal compressor. A model-free approach, that is, the Principal Component Analysis (PCA, has been employed to solve the fault detection issue. For the fault isolation purpose structured residuals have been adopted while an adaptive threshold has been designed in order to detect and to isolate the faults. To prove the goodness of the proposed FDI system, historical data of a nitrogen centrifugal compressor employed in a refinery plant are considered. Tests results show that detection and isolation of single as well as multiple faults are successfully achieved.

  1. Nuclear Power Plant Thermocouple Sensor-Fault Detection and Classification Using Deep Learning and Generalized Likelihood Ratio Test

    Science.gov (United States)

    Mandal, Shyamapada; Santhi, B.; Sridhar, S.; Vinolia, K.; Swaminathan, P.

    2017-06-01

    In this paper, an online fault detection and classification method is proposed for thermocouples used in nuclear power plants. In the proposed method, the fault data are detected by the classification method, which classifies the fault data from the normal data. Deep belief network (DBN), a technique for deep learning, is applied to classify the fault data. The DBN has a multilayer feature extraction scheme, which is highly sensitive to a small variation of data. Since the classification method is unable to detect the faulty sensor; therefore, a technique is proposed to identify the faulty sensor from the fault data. Finally, the composite statistical hypothesis test, namely generalized likelihood ratio test, is applied to compute the fault pattern of the faulty sensor signal based on the magnitude of the fault. The performance of the proposed method is validated by field data obtained from thermocouple sensors of the fast breeder test reactor.

  2. Experimental Investigation for RUAV's Actuator Fault Detections with AESMF

    Directory of Open Access Journals (Sweden)

    Dalei Song

    2015-07-01

    Full Text Available The adaptive extended set-membership filter (AESMF algorithm for robots' online modelling is today proposed for use in this field. Compared to the traditional ESMF, this novel filter method improves estimation accuracy under variable boundaries of unknown but bounded (UBB process noise, which is often caused by the uncertainties of robotic dynamics. However, the applicability and stability of the AESMF method have not been tested in detail or demonstrated for real robotic systems. In this research, AESMF is applied for the actuator fault detections of a rotor-craft unmanned air vehicle (RUAV. The stability of AESMF is firstly analysed using mathematics and actuator healthy coefficients (AHC are introduced for building the actuator failure model of RUAVs. AESMF is employed for the online boundary estimation of flight states and AHC parameters for fault tolerance control. Based on the proposed AESMF actuator fault estimation, flight experiments are conducted using a ServoHeli-40 RUAV platform and the flight results are compared with traditional ESMF and the adaptive extended Kalman filter (AEKF in order to demonstrate its effectiveness, as well as for suggesting improvements for the actuator failure detection of RUAVs.

  3. Detection of intermittent resistive faults in electronic systems based on the mixed-signal boundary-scan standard

    NARCIS (Netherlands)

    Kerkhoff, Hans G.; Ebrahimi, Hassan

    2015-01-01

    In avionics, like glide computers, the problem of No Faults Found (NFF) is a very serious and extremely costly affair. The rare occurrences and short bursts of these faults are the most difficult ones to detect and diagnose in the testing arena. Several techniques are now being developed in ICs by

  4. Fault detection using (PI) observers

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Stoustrup, J.; Shafai, B.

    1997-01-01

    The fault detection and isolation (FDI) problem in connection with Proportional Integral (PI) Observers is considered in this paper. A compact formulation of the FDI design problem using PI observers is given. An analysis of the FDI design problem is derived with respectt to the time domain...

  5. Fault detection for piecewise affine systems with application to ship propulsion systems.

    Science.gov (United States)

    Yang, Ying; Linlin, Li; Ding, Steven X; Qiu, Jianbin; Peng, Kaixiang

    2017-09-09

    In this paper, the design approach of non-synchronized diagnostic observer-based fault detection (FD) systems is investigated for piecewise affine processes via continuous piecewise Lyapunov functions. Considering that the dynamics of piecewise affine systems in different regions can be considerably different, the weighting matrices are used to weight the residual of each region, so as to optimize the fault detectability. A numerical example and a case study on a ship propulsion system are presented in the end to demonstrate the effectiveness of the proposed results. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  6. Robust fault detection of linear systems using a computationally efficient set-membership method

    DEFF Research Database (Denmark)

    Tabatabaeipour, Mojtaba; Bak, Thomas

    2014-01-01

    In this paper, a computationally efficient set-membership method for robust fault detection of linear systems is proposed. The method computes an interval outer-approximation of the output of the system that is consistent with the model, the bounds on noise and disturbance, and the past measureme...... is trivially parallelizable. The method is demonstrated for fault detection of a hydraulic pitch actuator of a wind turbine. We show the effectiveness of the proposed method by comparing our results with two zonotope-based set-membership methods....

  7. Observer-Based Fault Estimation and Accomodation for Dynamic Systems

    CERN Document Server

    Zhang, Ke; Shi, Peng

    2013-01-01

    Due to the increasing security and reliability demand of actual industrial process control systems, the study on fault diagnosis and fault tolerant control of dynamic systems has received considerable attention. Fault accommodation (FA) is one of effective methods that can be used to enhance system stability and reliability, so it has been widely and in-depth investigated and become a hot topic in recent years. Fault detection is used to monitor whether a fault occurs, which is the first step in FA. On the basis of fault detection, fault estimation (FE) is utilized to determine online the magnitude of the fault, which is a very important step because the additional controller is designed using the fault estimate. Compared with fault detection, the design difficulties of FE would increase a lot, so research on FE and accommodation is very challenging. Although there have been advancements reported on FE and accommodation for dynamic systems, the common methods at the present stage have design difficulties, whi...

  8. Faults detection approach using PCA and SOM algorithm in PMSG-WT system

    Directory of Open Access Journals (Sweden)

    Mohamed Lamine FADDA

    2016-07-01

    Full Text Available In this paper, a new approach for faults detection in observable data system wind turbine - permanent magnet synchronous generator (WT-PMSG, the studying objective, illustrate the combination (SOM-PCA to build Multi-local-PCA models faults detection in system (WT-PMSG, the performance of the method suggested to faults detection in system data, finding good results in simulation experiment.

  9. Fault detection and diagnosis using statistical control charts and artificial neural networks

    International Nuclear Information System (INIS)

    Leger, R.P.; Garland, W.J.; Poehlman, W.F.S.

    1995-01-01

    In order to operate a successful plant or process, continuous improvement must be made in the areas of safety, quality and reliability. Central to this continuous improvement is the early or proactive detection and correct diagnosis of process faults. This research examines the feasibility of using Cumulative Summation (CUSUM) Control Charts and artificial neural networks together for fault detection and diagnosis (FDD). The proposed FDD strategy was tested on a model of the heat transport system of a CANDU nuclear reactor. The results of the investigation indicate that a FDD system using CUSUM Control Charts and a Radial Basis Function (RBF) neural network is not only feasible but also of promising potential. The control charts and neural network are linked together by using a characteristic fault signature pattern for each fault which is to be detected and diagnosed. When tested, the system was able to eliminate all false alarms at steady state, promptly detect 6 fault conditions and correctly diagnose 5 out of the 6 faults. The diagnosis for the sixth fault was inconclusive. (author). 9 refs., 6 tabs., 7 figs

  10. Similarity ratio analysis for early stage fault detection with optical emission spectrometer in plasma etching process.

    Directory of Open Access Journals (Sweden)

    Jie Yang

    Full Text Available A Similarity Ratio Analysis (SRA method is proposed for early-stage Fault Detection (FD in plasma etching processes using real-time Optical Emission Spectrometer (OES data as input. The SRA method can help to realise a highly precise control system by detecting abnormal etch-rate faults in real-time during an etching process. The method processes spectrum scans at successive time points and uses a windowing mechanism over the time series to alleviate problems with timing uncertainties due to process shift from one process run to another. A SRA library is first built to capture features of a healthy etching process. By comparing with the SRA library, a Similarity Ratio (SR statistic is then calculated for each spectrum scan as the monitored process progresses. A fault detection mechanism, named 3-Warning-1-Alarm (3W1A, takes the SR values as inputs and triggers a system alarm when certain conditions are satisfied. This design reduces the chance of false alarm, and provides a reliable fault reporting service. The SRA method is demonstrated on a real semiconductor manufacturing dataset. The effectiveness of SRA-based fault detection is evaluated using a time-series SR test and also using a post-process SR test. The time-series SR provides an early-stage fault detection service, so less energy and materials will be wasted by faulty processing. The post-process SR provides a fault detection service with higher reliability than the time-series SR, but with fault testing conducted only after each process run completes.

  11. Experimental Fault Detection and Accomodation for an Agricultural Mobile Robot

    DEFF Research Database (Denmark)

    Østergaard, Kasper Zinck; Vinther, D.; Bisgaard, Morten

    2005-01-01

    This paper presents a systematic procedure to achieve fault tolerant capability for a four-wheel driven, four-wheel steered mobile robot moving in outdoor terrain. The procedure is exemplified through the paper by applying on a compass module. Detailed methods for fault detection and fault...

  12. From experiment to design -- Fault characterization and detection in parallel computer systems using computational accelerators

    Science.gov (United States)

    Yim, Keun Soo

    This dissertation summarizes experimental validation and co-design studies conducted to optimize the fault detection capabilities and overheads in hybrid computer systems (e.g., using CPUs and Graphics Processing Units, or GPUs), and consequently to improve the scalability of parallel computer systems using computational accelerators. The experimental validation studies were conducted to help us understand the failure characteristics of CPU-GPU hybrid computer systems under various types of hardware faults. The main characterization targets were faults that are difficult to detect and/or recover from, e.g., faults that cause long latency failures (Ch. 3), faults in dynamically allocated resources (Ch. 4), faults in GPUs (Ch. 5), faults in MPI programs (Ch. 6), and microarchitecture-level faults with specific timing features (Ch. 7). The co-design studies were based on the characterization results. One of the co-designed systems has a set of source-to-source translators that customize and strategically place error detectors in the source code of target GPU programs (Ch. 5). Another co-designed system uses an extension card to learn the normal behavioral and semantic execution patterns of message-passing processes executing on CPUs, and to detect abnormal behaviors of those parallel processes (Ch. 6). The third co-designed system is a co-processor that has a set of new instructions in order to support software-implemented fault detection techniques (Ch. 7). The work described in this dissertation gains more importance because heterogeneous processors have become an essential component of state-of-the-art supercomputers. GPUs were used in three of the five fastest supercomputers that were operating in 2011. Our work included comprehensive fault characterization studies in CPU-GPU hybrid computers. In CPUs, we monitored the target systems for a long period of time after injecting faults (a temporally comprehensive experiment), and injected faults into various types of

  13. Bearing fault detection utilizing group delay and the Hilbert-Huang transform

    International Nuclear Information System (INIS)

    Jin, Shuai; Lee, Sang-Kwon

    2017-01-01

    Vibration signals measured from a mechanical system are useful to detect system faults. Signal processing has been used to extract fault information in bearing systems. However, a wide vibration signal frequency band often affects the ability to obtain the effective fault features. In addition, a few oscillation components are not useful at the entire frequency band in a vibration signal. By contrast, useful fatigue information can be embedded in the noise oscillation components. Thus, a method to estimate which frequency band contains fault information utilizing group delay was proposed in this paper. Group delay as a measure of phase distortion can indicate the phase structure relationship in the frequency domain between original (with noise) and denoising signals. We used the empirical mode decomposition of a Hilbert-Huang transform to sift the useful intrinsic mode functions based on the results of group delay after determining the valuable frequency band. Finally, envelope analysis and the energy distribution after the Hilbert transform were used to complete the fault diagnosis. The practical bearing fault data, which were divided into inner and outer race faults, were used to verify the efficiency and quality of the proposed method

  14. Bearing fault detection utilizing group delay and the Hilbert-Huang transform

    Energy Technology Data Exchange (ETDEWEB)

    Jin, Shuai; Lee, Sang-Kwon [Inha University, Incheon (Korea, Republic of)

    2017-03-15

    Vibration signals measured from a mechanical system are useful to detect system faults. Signal processing has been used to extract fault information in bearing systems. However, a wide vibration signal frequency band often affects the ability to obtain the effective fault features. In addition, a few oscillation components are not useful at the entire frequency band in a vibration signal. By contrast, useful fatigue information can be embedded in the noise oscillation components. Thus, a method to estimate which frequency band contains fault information utilizing group delay was proposed in this paper. Group delay as a measure of phase distortion can indicate the phase structure relationship in the frequency domain between original (with noise) and denoising signals. We used the empirical mode decomposition of a Hilbert-Huang transform to sift the useful intrinsic mode functions based on the results of group delay after determining the valuable frequency band. Finally, envelope analysis and the energy distribution after the Hilbert transform were used to complete the fault diagnosis. The practical bearing fault data, which were divided into inner and outer race faults, were used to verify the efficiency and quality of the proposed method.

  15. Thermal-hydraulic modeling of deaerator and fault detection and diagnosis of measurement sensor

    International Nuclear Information System (INIS)

    Lee, Jung Woon; Park, Jae Chang; Kim, Jung Taek; Kim, Kyung Youn; Lee, In Soo; Kim, Bong Seok; Kang, Sook In

    2003-05-01

    It is important to note that an effective means to assure the reliability and security for the nuclear power plant is to detect and diagnose the faults (failures) as soon and as accurately as possible. The objective of the project is to develop model-based fault detection and diagnosis algorithm for the deaerator and evaluate the performance of the developed algorithm. The scope of the work can be classified into two categories. The one is state-space model-based FDD algorithm using Adaptive Estimator(AE) algorithm. The other is input-output model-based FDD algorithm using ART neural network. Extensive computer simulations for the real data obtained from Younggwang 3 and 4 FSAR are carried out to evaluate the performance in terms of speed and accuracy

  16. Karhunen Loeve Basis Used for Detection of Gearbox Faults in a Wind Turbine

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Stoustrup, Jakob

    2014-01-01

    Reliability and sustainability of wind turbines increase in importance as wind turbines contribute with increasing power generation to the world's power grids. One possible way to achieve this is by using advanced fault detection and isolation methods in wind turbines based on the measurements...... provided to the control system. In this paper a Karhunen-Loeve basis approach is designed for detecting changes in frequency response from rotating parts like a gearbox. The potential of this method is shown by applying it to an established Wind Turbine FDI and FTC Benchmark model. These faults...

  17. Robust fault detection and isolation technique for single-input/single-output closed-loop control systems that exhibit actuator and sensor faults

    DEFF Research Database (Denmark)

    Izadi-Zamanabadi, Roozbeh; Alavi, S. M. Mahdi; Hayes, M. J.

    2008-01-01

    An integrated quantitative feedback design and frequency-based fault detection and isolation (FDI) approach is presented for single-input/single-output systems. A novel design methodology, based on shaping the system frequency response, is proposed to generate an appropriate residual signal...

  18. Fault detection and isolation of high temperature proton exchange membrane fuel cell stack under the influence of degradation

    Science.gov (United States)

    Jeppesen, Christian; Araya, Samuel Simon; Sahlin, Simon Lennart; Thomas, Sobi; Andreasen, Søren Juhl; Kær, Søren Knudsen

    2017-08-01

    This study proposes a data-drive impedance-based methodology for fault detection and isolation of low and high cathode stoichiometry, high CO concentration in the anode gas, high methanol vapour concentrations in the anode gas and low anode stoichiometry, for high temperature PEM fuel cells. The fault detection and isolation algorithm is based on an artificial neural network classifier, which uses three extracted features as input. Two of the proposed features are based on angles in the impedance spectrum, and are therefore relative to specific points, and shown to be independent of degradation, contrary to other available feature extraction methods in the literature. The experimental data is based on a 35 day experiment, where 2010 unique electrochemical impedance spectroscopy measurements were recorded. The test of the algorithm resulted in a good detectability of the faults, except for high methanol vapour concentration in the anode gas fault, which was found to be difficult to distinguish from a normal operational data. The achieved accuracy for faults related to CO pollution, anode- and cathode stoichiometry is 100% success rate. Overall global accuracy on the test data is 94.6%.

  19. Using Order Tracking Analysis Method to Detect the Angle Faults of Blades on Wind Turbine

    DEFF Research Database (Denmark)

    Li, Pengfei; Hu, Weihao; Liu, Juncheng

    2016-01-01

    The angle faults of blades on wind turbines are usually included in the set angle fault and the pitch angle fault. They are occupied with a high proportion in all wind turbine faults. Compare with the traditional fault detection methods, using order tracking analysis method to detect angle faults....... By analyzing and reconstructing the fault signals, it is easy to detect the fault characteristic frequency and see the characteristic frequencies of angle faults depend on the shaft rotating frequency, which is known as the 1P frequency and 3P frequency distinctly....

  20. Detecting Faults By Use Of Hidden Markov Models

    Science.gov (United States)

    Smyth, Padhraic J.

    1995-01-01

    Frequency of false alarms reduced. Faults in complicated dynamic system (e.g., antenna-aiming system, telecommunication network, or human heart) detected automatically by method of automated, continuous monitoring. Obtains time-series data by sampling multiple sensor outputs at discrete intervals of t and processes data via algorithm determining whether system in normal or faulty state. Algorithm implements, among other things, hidden first-order temporal Markov model of states of system. Mathematical model of dynamics of system not needed. Present method is "prior" method mentioned in "Improved Hidden-Markov-Model Method of Detecting Faults" (NPO-18982).

  1. Comparative Study of Parametric and Non-parametric Approaches in Fault Detection and Isolation

    DEFF Research Database (Denmark)

    Katebi, S.D.; Blanke, M.; Katebi, M.R.

    This report describes a comparative study between two approaches to fault detection and isolation in dynamic systems. The first approach uses a parametric model of the system. The main components of such techniques are residual and signature generation for processing and analyzing. The second...... approach is non-parametric in the sense that the signature analysis is only dependent on the frequency or time domain information extracted directly from the input-output signals. Based on these approaches, two different fault monitoring schemes are developed where the feature extraction and fault decision...

  2. Fault Detection Using the Zero Crossing Rate | Osuagwu | Nigerian ...

    African Journals Online (AJOL)

    A method of fault detection based on the zero crossing rate of the signal, Z1, and the zero crossing rate of the first order difference signal. Z2, is presented. It is shown that the parameter pair (Z1, Z2) possesses adequate discriminating potential to classify a signature as good or defective. The parameter pair also carries ...

  3. Fault detection for discrete-time LPV systems using interval observers

    Science.gov (United States)

    Zhang, Zhi-Hui; Yang, Guang-Hong

    2017-10-01

    This paper is concerned with the fault detection (FD) problem for discrete-time linear parameter-varying systems subject to bounded disturbances. A parameter-dependent FD interval observer is designed based on parameter-dependent Lyapunov and slack matrices. The design method is presented by translating the parameter-dependent linear matrix inequalities (LMIs) into finite ones. In contrast to the existing results based on parameter-independent and diagonal Lyapunov matrices, the derived disturbance attenuation, fault sensitivity and nonnegative conditions lead to less conservative LMI characterisations. Furthermore, without the need to design the residual evaluation functions and thresholds, the residual intervals generated by the interval observers are used directly for FD decision. Finally, simulation results are presented for showing the effectiveness and superiority of the proposed method.

  4. Fault detection of flywheel system based on clustering and principal component analysis

    Directory of Open Access Journals (Sweden)

    Wang Rixin

    2015-12-01

    Full Text Available Considering the nonlinear, multifunctional properties of double-flywheel with closed-loop control, a two-step method including clustering and principal component analysis is proposed to detect the two faults in the multifunctional flywheels. At the first step of the proposed algorithm, clustering is taken as feature recognition to check the instructions of “integrated power and attitude control” system, such as attitude control, energy storage or energy discharge. These commands will ask the flywheel system to work in different operation modes. Therefore, the relationship of parameters in different operations can define the cluster structure of training data. Ordering points to identify the clustering structure (OPTICS can automatically identify these clusters by the reachability-plot. K-means algorithm can divide the training data into the corresponding operations according to the reachability-plot. Finally, the last step of proposed model is used to define the relationship of parameters in each operation through the principal component analysis (PCA method. Compared with the PCA model, the proposed approach is capable of identifying the new clusters and learning the new behavior of incoming data. The simulation results show that it can effectively detect the faults in the multifunctional flywheels system.

  5. A Novel Approach for Eccentricity Fault Detection in Squirrel Cage Induction Motors

    Directory of Open Access Journals (Sweden)

    Mehdi Ahmadi

    2013-01-01

    Full Text Available In this paper, static eccentricity fault detection in induction motors is studied. Two dimensional finite element method (2D-FEM is used for faultless and eccentric condition modeling in induction motors. Also current and speed signals are compared in two experimental and simulation cases for model validating. For fault detection, fast Fourier transform is used at first. In this method, high order harmonics with small amplitude can alarms the fault occurrence. For this reason, the fault detection process is difficult.To overcome these drawbacks, it is suggested that two test coils contrive around the air-gap. So, any changes in air-gap can be detected easily. Moreover this test coils are used in open circuit case. So, these test coils do not effect on motor dynamics. Also, the results show that modulated voltage can be alarm the fault occurrence, type and percent well.

  6. An energy kurtosis demodulation technique for signal denoising and bearing fault detection

    International Nuclear Information System (INIS)

    Wang, Wilson; Lee, Hewen

    2013-01-01

    Rolling element bearings are commonly used in rotary machinery. Reliable bearing fault detection techniques are very useful in industries for predictive maintenance operations. Bearing fault detection still remains a very challenging task especially when defects occur on rotating bearing components because the fault-related features are non-stationary in nature. In this work, an energy kurtosis demodulation (EKD) technique is proposed for bearing fault detection especially for non-stationary signature analysis. The proposed EKD technique firstly denoises the signal by using a maximum kurtosis deconvolution filter to counteract the effect of signal transmission path so as to highlight defect-associated impulses. Next, the denoised signal is modulated over several frequency bands; a novel signature integration strategy is proposed to enhance feature characteristics. The effectiveness of the proposed EKD fault detection technique is verified by a series of experimental tests corresponding to different bearing conditions. (paper)

  7. Operations management system advanced automation: Fault detection isolation and recovery prototyping

    Science.gov (United States)

    Hanson, Matt

    1990-01-01

    The purpose of this project is to address the global fault detection, isolation and recovery (FDIR) requirements for Operation's Management System (OMS) automation within the Space Station Freedom program. This shall be accomplished by developing a selected FDIR prototype for the Space Station Freedom distributed processing systems. The prototype shall be based on advanced automation methodologies in addition to traditional software methods to meet the requirements for automation. A secondary objective is to expand the scope of the prototyping to encompass multiple aspects of station-wide fault management (SWFM) as discussed in OMS requirements documentation.

  8. Enhanced dynamic data-driven fault detection approach: Application to a two-tank heater system

    KAUST Repository

    Harrou, Fouzi

    2018-02-12

    Principal components analysis (PCA) has been intensively studied and used in monitoring industrial systems. However, data generated from chemical processes are usually correlated in time due to process dynamics, which makes the fault detection based on PCA approach a challenging task. Accounting for the dynamic nature of data can also reflect the performance of the designed fault detection approaches. In PCA-based methods, this dynamic characteristic of the data can be accounted for by using dynamic PCA (DPCA), in which lagged variables are used in the PCA model to capture the time evolution of the process. This paper presents a new approach that combines the DPCA to account for autocorrelation in data and generalized likelihood ratio (GLR) test to detect faults. A DPCA model is applied to perform dimension reduction while appropriately considering the temporal relationships in the data. Specifically, the proposed approach uses the DPCA to generate residuals, and then apply GLR test to reveal any abnormality. The performances of the proposed method are evaluated through a continuous stirred tank heater system.

  9. Fault Features Extraction and Identification based Rolling Bearing Fault Diagnosis

    International Nuclear Information System (INIS)

    Qin, B; Sun, G D; Zhang L Y; Wang J G; HU, J

    2017-01-01

    For the fault classification model based on extreme learning machine (ELM), the diagnosis accuracy and stability of rolling bearing is greatly influenced by a critical parameter, which is the number of nodes in hidden layer of ELM. An adaptive adjustment strategy is proposed based on vibrational mode decomposition, permutation entropy, and nuclear kernel extreme learning machine to determine the tunable parameter. First, the vibration signals are measured and then decomposed into different fault feature models based on variation mode decomposition. Then, fault feature of each model is formed to a high dimensional feature vector set based on permutation entropy. Second, the ELM output function is expressed by the inner product of Gauss kernel function to adaptively determine the number of hidden layer nodes. Finally, the high dimension feature vector set is used as the input to establish the kernel ELM rolling bearing fault classification model, and the classification and identification of different fault states of rolling bearings are carried out. In comparison with the fault classification methods based on support vector machine and ELM, the experimental results show that the proposed method has higher classification accuracy and better generalization ability. (paper)

  10. Fault detection and isolation of high temperature proton exchange membrane fuel cell stack under the influence of degradation

    DEFF Research Database (Denmark)

    Jeppesen, Christian; Araya, Samuel Simon; Sahlin, Simon Lennart

    2017-01-01

    This study proposes a data-drive impedance-based methodology for fault detection and isolation of low and high cathode stoichiometry, high CO concentration in the anode gas, high methanol vapour concentrations in the anode gas and low anode stoichiometry, for high temperature PEM fuel cells....... The fault detection and isolation algorithm is based on an artificial neural network classifier, which uses three extracted features as input. Two of the proposed features are based on angles in the impedance spectrum, and are therefore relative to specific points, and shown to be independent of degradation......, contrary to other available feature extraction methods in the literature. The experimental data is based on a 35 day experiment, where 2010 unique electrochemical impedance spectroscopy measurements were recorded. The test of the algorithm resulted in a good detectability of the faults, except for high...

  11. Remote sensing analysis for fault-zones detection in the Central Andean Plateau (Catamarca, Argentina)

    Science.gov (United States)

    Traforti, Anna; Massironi, Matteo; Zampieri, Dario; Carli, Cristian

    2015-04-01

    Remote sensing techniques have been extensively used to detect the structural framework of investigated areas, which includes lineaments, fault zones and fracture patterns. The identification of these features is fundamental in exploration geology, as it allows the definition of suitable sites for the exploitation of different resources (e.g. ore mineral, hydrocarbon, geothermal energy and groundwater). Remote sensing techniques, typically adopted in fault identification, have been applied to assess the geological and structural framework of the Laguna Blanca area (26°35'S-66°49'W). This area represents a sector of the south-central Andes localized in the Argentina region of Catamarca, along the south-eastern margin of the Puna plateau. The study area is characterized by a Precambrian low-grade metamorphic basement intruded by Ordovician granitoids. These rocks are unconformably covered by a volcano-sedimentary sequence of Miocene age, followed by volcanic and volcaniclastic rocks of Upper Miocene to Plio-Pleistocene age. All these units are cut by two systems of major faults, locally characterized by 15-20 m wide damage zones. The detection of main tectonic lineaments in the study area was firstly carried out by classical procedures: image sharpening of Landsat 7 ETM+ images, directional filters applied to ASTER images, medium resolution Digital Elevation Models analysis (SRTM and ASTER GDEM) and hill shades interpretation. In addition, a new approach in fault zone identification, based on multispectral satellite images classification, has been tested in the Laguna Blanca area and in other sectors of south-central Andes. In this perspective, several prominent fault zones affecting basement and granitoid rocks have been sampled. The collected fault gouge samples have been analyzed with a Field-Pro spectrophotometer mounted on a goniometer. We acquired bidirectional reflectance spectra, from 0.35μm to 2.5μm with 1nm spectral sampling, of the sampled fault rocks

  12. New method of silicon photovoltaic panel fault detection using impedance spectroscopy

    DEFF Research Database (Denmark)

    Symonowicz, Joanna Karolina; Riedel, Nicholas; Thorsteinsson, Sune

    2017-01-01

    The aim of our project is to develop a new method for photovoltaic (PV) panel fault detection based on analysing its impedance spectra (IS). Although this technique was successful in assessing the state of degradation of fuel cells and batteries [1, 2], it has never been applied to PV cells...... on a wide scale. In this paper, we show that, unlike current-voltage (I-V) tests, the IS method is capable of early detection of changes in PV panel parameters due to microcracks and potential-induced degradation (PID). Although our measurements are only successful under dark conditions, the results...... are similar for both laboratory environment and for outdoor tests in various weather conditions. A fully developed IS technique, accounting for all kinds of most common PV panel degradation types, would surpass the existing PV fault detection methods then it comes to cost and accuracy [3,4]....

  13. Fault diagnostics for turbo-shaft engine sensors based on a simplified on-board model.

    Science.gov (United States)

    Lu, Feng; Huang, Jinquan; Xing, Yaodong

    2012-01-01

    Combining a simplified on-board turbo-shaft model with sensor fault diagnostic logic, a model-based sensor fault diagnosis method is proposed. The existing fault diagnosis method for turbo-shaft engine key sensors is mainly based on a double redundancies technique, and this can't be satisfied in some occasions as lack of judgment. The simplified on-board model provides the analytical third channel against which the dual channel measurements are compared, while the hardware redundancy will increase the structure complexity and weight. The simplified turbo-shaft model contains the gas generator model and the power turbine model with loads, this is built up via dynamic parameters method. Sensor fault detection, diagnosis (FDD) logic is designed, and two types of sensor failures, such as the step faults and the drift faults, are simulated. When the discrepancy among the triplex channels exceeds a tolerance level, the fault diagnosis logic determines the cause of the difference. Through this approach, the sensor fault diagnosis system achieves the objectives of anomaly detection, sensor fault diagnosis and redundancy recovery. Finally, experiments on this method are carried out on a turbo-shaft engine, and two types of faults under different channel combinations are presented. The experimental results show that the proposed method for sensor fault diagnostics is efficient.

  14. Fault Diagnostics for Turbo-Shaft Engine Sensors Based on a Simplified On-Board Model

    Directory of Open Access Journals (Sweden)

    Yaodong Xing

    2012-08-01

    Full Text Available Combining a simplified on-board turbo-shaft model with sensor fault diagnostic logic, a model-based sensor fault diagnosis method is proposed. The existing fault diagnosis method for turbo-shaft engine key sensors is mainly based on a double redundancies technique, and this can’t be satisfied in some occasions as lack of judgment. The simplified on-board model provides the analytical third channel against which the dual channel measurements are compared, while the hardware redundancy will increase the structure complexity and weight. The simplified turbo-shaft model contains the gas generator model and the power turbine model with loads, this is built up via dynamic parameters method. Sensor fault detection, diagnosis (FDD logic is designed, and two types of sensor failures, such as the step faults and the drift faults, are simulated. When the discrepancy among the triplex channels exceeds a tolerance level, the fault diagnosis logic determines the cause of the difference. Through this approach, the sensor fault diagnosis system achieves the objectives of anomaly detection, sensor fault diagnosis and redundancy recovery. Finally, experiments on this method are carried out on a turbo-shaft engine, and two types of faults under different channel combinations are presented. The experimental results show that the proposed method for sensor fault diagnostics is efficient.

  15. An angle-based subspace anomaly detection approach to high-dimensional data: With an application to industrial fault detection

    International Nuclear Information System (INIS)

    Zhang, Liangwei; Lin, Jing; Karim, Ramin

    2015-01-01

    The accuracy of traditional anomaly detection techniques implemented on full-dimensional spaces degrades significantly as dimensionality increases, thereby hampering many real-world applications. This work proposes an approach to selecting meaningful feature subspace and conducting anomaly detection in the corresponding subspace projection. The aim is to maintain the detection accuracy in high-dimensional circumstances. The suggested approach assesses the angle between all pairs of two lines for one specific anomaly candidate: the first line is connected by the relevant data point and the center of its adjacent points; the other line is one of the axis-parallel lines. Those dimensions which have a relatively small angle with the first line are then chosen to constitute the axis-parallel subspace for the candidate. Next, a normalized Mahalanobis distance is introduced to measure the local outlier-ness of an object in the subspace projection. To comprehensively compare the proposed algorithm with several existing anomaly detection techniques, we constructed artificial datasets with various high-dimensional settings and found the algorithm displayed superior accuracy. A further experiment on an industrial dataset demonstrated the applicability of the proposed algorithm in fault detection tasks and highlighted another of its merits, namely, to provide preliminary interpretation of abnormality through feature ordering in relevant subspaces. - Highlights: • An anomaly detection approach for high-dimensional reliability data is proposed. • The approach selects relevant subspaces by assessing vectorial angles. • The novel ABSAD approach displays superior accuracy over other alternatives. • Numerical illustration approves its efficacy in fault detection applications

  16. Incipient Fault Detection and Isolation of Field Devices in Nuclear Power Systems Using Principal Component Analysis

    International Nuclear Information System (INIS)

    Kaistha, Nitin; Upadhyaya, Belle R.

    2001-01-01

    An integrated method for the detection and isolation of incipient faults in common field devices, such as sensors and actuators, using plant operational data is presented. The approach is based on the premise that data for normal operation lie on a surface and abnormal situations lead to deviations from the surface in a particular way. Statistically significant deviations from the surface result in the detection of faults, and the characteristic directions of deviations are used for isolation of one or more faults from the set of typical faults. Principal component analysis (PCA), a multivariate data-driven technique, is used to capture the relationships in the data and fit a hyperplane to the data. The fault direction for each of the scenarios is obtained using the singular value decomposition on the state and control function prediction errors, and fault isolation is then accomplished from projections on the fault directions. This approach is demonstrated for a simulated pressurized water reactor steam generator system and for a laboratory process control system under single device fault conditions. Enhanced fault isolation capability is also illustrated by incorporating realistic nonlinear terms in the PCA data matrix

  17. Active fault detection and isolation of discrete-time linear time-varying systems: a set-membership approach

    DEFF Research Database (Denmark)

    Tabatabaeipour, Mojtaba

    2013-01-01

    Active fault detection and isolation (AFDI) is used for detection and isolation of faults that are hidden in the normal operation because of a low excitation signal or due to the regulatory actions of the controller. In this paper, a new AFDI method based on set-membership approaches is proposed...... un-falsified, the AFDI method is used to generate an auxiliary signal that is injected into the system for detection and isolation of faults that remain otherwise hidden or non-isolated using passive FDI (PFDI) methods. Having the set-valued estimation of the states for each model, the proposed AFDI...... method finds an optimal input signal that guarantees FDI in a finite time horizon. The input signal is updated at each iteration in a decreasing receding horizon manner based on the set-valued estimation of the current states and un-falsified models at the current sample time. The problem is solved...

  18. Automated Fault Detection for DIII-D Tokamak Experiments

    International Nuclear Information System (INIS)

    Walker, M.L.; Scoville, J.T.; Johnson, R.D.; Hyatt, A.W.; Lee, J.

    1999-01-01

    An automated fault detection software system has been developed and was used during 1999 DIII-D plasma operations. The Fault Identification and Communication System (FICS) executes automatically after every plasma discharge to check dozens of subsystems for proper operation and communicates the test results to the tokamak operator. This system is now used routinely during DIII-D operations and has led to an increase in tokamak productivity

  19. Robust filtering and fault detection of switched delay systems

    CERN Document Server

    Wang, Dong; Wang, Wei

    2013-01-01

    Switched delay systems appear in a wide field of applications including networked control systems, power systems, memristive systems. Though the large amount of ideas with respect to such systems have generated, until now, it still lacks a framework to focus on filter design and fault detection issues which are relevant to life safety and property loss. Beginning with the comprehensive coverage of the new developments in the analysis and control synthesis for switched delay systems, the monograph not only provides a systematic approach to designing the filter and detecting the fault of switched delay systems, but it also covers the model reduction issues. Specific topics covered include: (1) Arbitrary switching signal where delay-independent and delay-dependent conditions are presented by proposing a linearization technique. (2) Average dwell time where a weighted Lyapunov function is come up with dealing with filter design and fault detection issues beside taking model reduction problems. The monograph is in...

  20. Fault detection of a Five-Phase Permanent-Magnet Machine

    DEFF Research Database (Denmark)

    Bianchini, Claudio; Matzen, Torben N.; Bianchi, Nicola

    2008-01-01

    The paper focuses on the fault detection of a five-phase Permanent-Magnet (PM) machine. This machine has been de-signed for fault tolerant applications, and it is characterised by a mutual inductance equal to zero and a high self inductance, with the purpose to limit the short circuit current...

  1. Incipient Stator Insulation Fault Detection of Permanent Magnet Synchronous Wind Generators Based on Hilbert–Huang Transformation

    DEFF Research Database (Denmark)

    Wang, Chao; Liu, Xiao; Chen, Zhe

    2014-01-01

    of insulation degradation of one turn in the winding of a PMSWG. Cosimulation method by combining finite element model and external circuits is used. Hilbert–Huang transformation is applied to detect the very early stage fault in interturn insulation by analyzing the stator current. Detection results show...

  2. Fault detection and fault-tolerant control for nonlinear systems

    CERN Document Server

    Li, Linlin

    2016-01-01

    Linlin Li addresses the analysis and design issues of observer-based FD and FTC for nonlinear systems. The author analyses the existence conditions for the nonlinear observer-based FD systems to gain a deeper insight into the construction of FD systems. Aided by the T-S fuzzy technique, she recommends different design schemes, among them the L_inf/L_2 type of FD systems. The derived FD and FTC approaches are verified by two benchmark processes. Contents Overview of FD and FTC Technology Configuration of Nonlinear Observer-Based FD Systems Design of L2 nonlinear Observer-Based FD Systems Design of Weighted Fuzzy Observer-Based FD Systems FTC Configurations for Nonlinear Systems< Application to Benchmark Processes Target Groups Researchers and students in the field of engineering with a focus on fault diagnosis and fault-tolerant control fields The Author Dr. Linlin Li completed her dissertation under the supervision of Prof. Steven X. Ding at the Faculty of Engineering, University of Duisburg-Essen, Germany...

  3. Detection of Sensor Faults in Small Helicopter UAVs Using Observer/Kalman Filter Identification

    Directory of Open Access Journals (Sweden)

    Guillermo Heredia

    2011-01-01

    Full Text Available Reliability is a critical issue in navigation of unmanned aerial vehicles (UAVs since there is no human pilot that can react to any abnormal situation. Due to size and cost limitations, redundant sensor schemes and aeronautical-grade navigation sensors used in large aircrafts cannot be installed in small UAVs. Therefore, other approaches like analytical redundancy should be used to detect faults in navigation sensors and increase reliability. This paper presents a sensor fault detection and diagnosis system for small autonomous helicopters based on analytical redundancy. Fault detection is accomplished by evaluating any significant change in the behaviour of the vehicle with respect to the fault-free behaviour, which is estimated by using an observer. The observer is obtained from input-output experimental data with the Observer/Kalman Filter Identification (OKID method. The OKID method is able to identify the system and an observer with properties similar to a Kalman filter, directly from input-output experimental data. Results are similar to the Kalman filter, but, with the proposed method, there is no need to estimate neither system matrices nor sensor and process noise covariance matrices. The system has been tested with real helicopter flight data, and the results compared with other methods.

  4. Monitoring a robot swarm using a data-driven fault detection approach

    KAUST Repository

    Khaldi, Belkacem

    2017-06-30

    Using swarm robotics system, with one or more faulty robots, to accomplish specific tasks may lead to degradation in performances complying with the target requirements. In such circumstances, robot swarms require continuous monitoring to detect abnormal events and to sustain normal operations. In this paper, an innovative exogenous fault detection method for monitoring robots swarm is presented. The method merges the flexibility of principal component analysis (PCA) models and the greater sensitivity of the exponentially-weighted moving average (EWMA) and cumulative sum (CUSUM) control charts to insidious changes. The method is tested and evaluated on a swarm of simulated foot-bot robots performing a circle formation task, via the viscoelastic control model. We illustrate through simulated data collected from the ARGoS simulator that a significant improvement in fault detection can be obtained by using the proposed method where compared to the conventional PCA-based methods (i.e., T2 and Q).

  5. A Novel Busbar Protection Based on the Average Product of Fault Components

    Directory of Open Access Journals (Sweden)

    Guibin Zou

    2018-05-01

    Full Text Available This paper proposes an original busbar protection method, based on the characteristics of the fault components. The method firstly extracts the fault components of the current and voltage after the occurrence of a fault, secondly it uses a novel phase-mode transformation array to obtain the aerial mode components, and lastly, it obtains the sign of the average product of the aerial mode voltage and current. For a fault on the busbar, the average products that are detected on all of the lines that are linked to the faulted busbar are all positive within a specific duration of the post-fault. However, for a fault at any one of these lines, the average product that has been detected on the faulted line is negative, while those on the non-faulted lines are positive. On the basis of the characteristic difference that is mentioned above, the identification criterion of the fault direction is established. Through comparing the fault directions on all of the lines, the busbar protection can quickly discriminate between an internal fault and an external fault. By utilizing the PSCAD/EMTDC software (4.6.0.0, Manitoba HVDC Research Centre, Winnipeg, MB, Canada, a typical 500 kV busbar model, with one and a half circuit breakers configuration, was constructed. The simulation results show that the proposed busbar protection has a good adjustability, high reliability, and rapid operation speed.

  6. Simultaneous Event-Triggered Fault Detection and Estimation for Stochastic Systems Subject to Deception Attacks.

    Science.gov (United States)

    Li, Yunji; Wu, QingE; Peng, Li

    2018-01-23

    In this paper, a synthesized design of fault-detection filter and fault estimator is considered for a class of discrete-time stochastic systems in the framework of event-triggered transmission scheme subject to unknown disturbances and deception attacks. A random variable obeying the Bernoulli distribution is employed to characterize the phenomena of the randomly occurring deception attacks. To achieve a fault-detection residual is only sensitive to faults while robust to disturbances, a coordinate transformation approach is exploited. This approach can transform the considered system into two subsystems and the unknown disturbances are removed from one of the subsystems. The gain of fault-detection filter is derived by minimizing an upper bound of filter error covariance. Meanwhile, system faults can be reconstructed by the remote fault estimator. An recursive approach is developed to obtain fault estimator gains as well as guarantee the fault estimator performance. Furthermore, the corresponding event-triggered sensor data transmission scheme is also presented for improving working-life of the wireless sensor node when measurement information are aperiodically transmitted. Finally, a scaled version of an industrial system consisting of local PC, remote estimator and wireless sensor node is used to experimentally evaluate the proposed theoretical results. In particular, a novel fault-alarming strategy is proposed so that the real-time capacity of fault-detection is guaranteed when the event condition is triggered.

  7. Case-Based Fault Diagnostic System

    International Nuclear Information System (INIS)

    Mohamed, A.H.

    2014-01-01

    Nowadays, case-based fault diagnostic (CBFD) systems have become important and widely applied problem solving technologies. They are based on the assumption that “similar faults have similar diagnosis”. On the other hand, CBFD systems still suffer from some limitations. Common ones of them are: (1) failure of CBFD to have the needed diagnosis for the new faults that have no similar cases in the case library. (2) Limited memorization when increasing the number of stored cases in the library. The proposed research introduces incorporating the neural network into the case based system to enable the system to diagnose all the faults. Neural networks have proved their success in the classification and diagnosis problems. The suggested system uses the neural network to diagnose the new faults (cases) that cannot be diagnosed by the traditional CBR diagnostic system. Besides, the proposed system can use the another neural network to control adding and deleting the cases in the library to manage the size of the cases in the case library. However, the suggested system has improved the performance of the case based fault diagnostic system when applied for the motor rolling bearing as a case of study

  8. Detecting Faults in Southern California using Computer-Vision Techniques and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) Interferometry

    Science.gov (United States)

    Barba, M.; Rains, C.; von Dassow, W.; Parker, J. W.; Glasscoe, M. T.

    2013-12-01

    Knowing the location and behavior of active faults is essential for earthquake hazard assessment and disaster response. In Interferometric Synthetic Aperture Radar (InSAR) images, faults are revealed as linear discontinuities. Currently, interferograms are manually inspected to locate faults. During the summer of 2013, the NASA-JPL DEVELOP California Disasters team contributed to the development of a method to expedite fault detection in California using remote-sensing technology. The team utilized InSAR images created from polarimetric L-band data from NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) project. A computer-vision technique known as 'edge-detection' was used to automate the fault-identification process. We tested and refined an edge-detection algorithm under development through NASA's Earthquake Data Enhanced Cyber-Infrastructure for Disaster Evaluation and Response (E-DECIDER) project. To optimize the algorithm we used both UAVSAR interferograms and synthetic interferograms generated through Disloc, a web-based modeling program available through NASA's QuakeSim project. The edge-detection algorithm detected seismic, aseismic, and co-seismic slip along faults that were identified and compared with databases of known fault systems. Our optimization process was the first step toward integration of the edge-detection code into E-DECIDER to provide decision support for earthquake preparation and disaster management. E-DECIDER partners that will use the edge-detection code include the California Earthquake Clearinghouse and the US Department of Homeland Security through delivery of products using the Unified Incident Command and Decision Support (UICDS) service. Through these partnerships, researchers, earthquake disaster response teams, and policy-makers will be able to use this new methodology to examine the details of ground and fault motions for moderate to large earthquakes. Following an earthquake, the newly discovered faults can

  9. Fault Detection in High Speed Helical Gears Considering Signal Processing Method in Real Simulation

    Directory of Open Access Journals (Sweden)

    Amir Ali Tabatabai Adnani

    Full Text Available Abstract In the present study, in order to detect the fault of the gearmeshs, two engaged gears based on research department of a major automotive company have been modeled. First off, by using the CATIA software the fault was induced to the output gear. Then, the faulty gearmesh and non-faulty gearmesh is modeled to find the fault pattern to predict and estimate the failure of the gearmesh. The induced defect is according to the frequently practical fault that takes place to the teeth of gears. In order to record the acceleration signals to calculate the decomposition algorithm, mount the accelerometer on accessible place of the output shaft to recognize the pattern. Then, for more realistic simulation, noise is added to the output signal. At the first step by means of Butterworth low pass digital, the noise has to be removed from signals after that by using the Empirical Mode Decomposition (EMD, signals have decomposed into the Instinct Mode Function (IMF and every IMF were tested by using the Instantaneous Frequency (IF in way of Hillbert Transform (HT. For this purpose a code was developed in MATLAB software. Then, in order to detect the presence of the fault the frequency spectrum of IMF's are created and defect is detected in gearmesh frequency of the spectrum.

  10. A Framework and Classification for Fault Detection Approaches in Wireless Sensor Networks with an Energy Efficiency Perspective

    DEFF Research Database (Denmark)

    Zhang, Yue; Dragoni, Nicola; Wang, Jiangtao

    2015-01-01

    efficiency to facilitate the design of fault detection methods and the evaluation of their energy efficiency. Following the same design principle of the fault detection framework, the paper proposes a classification for fault detection approaches. The classification is applied to a number of fault detection...

  11. Signal processing for solar array monitoring, fault detection, and optimization

    CERN Document Server

    Braun, Henry; Spanias, Andreas

    2012-01-01

    Although the solar energy industry has experienced rapid growth recently, high-level management of photovoltaic (PV) arrays has remained an open problem. As sensing and monitoring technology continues to improve, there is an opportunity to deploy sensors in PV arrays in order to improve their management. In this book, we examine the potential role of sensing and monitoring technology in a PV context, focusing on the areas of fault detection, topology optimization, and performance evaluation/data visualization. First, several types of commonly occurring PV array faults are considered and detection algorithms are described. Next, the potential for dynamic optimization of an array's topology is discussed, with a focus on mitigation of fault conditions and optimization of power output under non-fault conditions. Finally, monitoring system design considerations such as type and accuracy of measurements, sampling rate, and communication protocols are considered. It is our hope that the benefits of monitoring presen...

  12. Model-based fault detection for generator cooling system in wind turbines using SCADA data

    DEFF Research Database (Denmark)

    Borchersen, Anders Bech; Kinnaert, Michel

    2016-01-01

    In this work, an early fault detection system for the generator cooling of wind turbines is presented and tested. It relies on a hybrid model of the cooling system. The parameters of the generator model are estimated by an extended Kalman filter. The estimated parameters are then processed by an ...

  13. Development of Fault Models for Hybrid Fault Detection and Diagnostics Algorithm: October 1, 2014 -- May 5, 2015

    Energy Technology Data Exchange (ETDEWEB)

    Cheung, Howard [Purdue Univ., West Lafayette, IN (United States); Braun, James E. [Purdue Univ., West Lafayette, IN (United States)

    2015-12-31

    This report describes models of building faults created for OpenStudio to support the ongoing development of fault detection and diagnostic (FDD) algorithms at the National Renewable Energy Laboratory. Building faults are operating abnormalities that degrade building performance, such as using more energy than normal operation, failing to maintain building temperatures according to the thermostat set points, etc. Models of building faults in OpenStudio can be used to estimate fault impacts on building performance and to develop and evaluate FDD algorithms. The aim of the project is to develop fault models of typical heating, ventilating and air conditioning (HVAC) equipment in the United States, and the fault models in this report are grouped as control faults, sensor faults, packaged and split air conditioner faults, water-cooled chiller faults, and other uncategorized faults. The control fault models simulate impacts of inappropriate thermostat control schemes such as an incorrect thermostat set point in unoccupied hours and manual changes of thermostat set point due to extreme outside temperature. Sensor fault models focus on the modeling of sensor biases including economizer relative humidity sensor bias, supply air temperature sensor bias, and water circuit temperature sensor bias. Packaged and split air conditioner fault models simulate refrigerant undercharging, condenser fouling, condenser fan motor efficiency degradation, non-condensable entrainment in refrigerant, and liquid line restriction. Other fault models that are uncategorized include duct fouling, excessive infiltration into the building, and blower and pump motor degradation.

  14. Development of a variable structure-based fault detection and diagnosis strategy applied to an electromechanical system

    Science.gov (United States)

    Gadsden, S. Andrew; Kirubarajan, T.

    2017-05-01

    Signal processing techniques are prevalent in a wide range of fields: control, target tracking, telecommunications, robotics, fault detection and diagnosis, and even stock market analysis, to name a few. Although first introduced in the 1950s, the most popular method used for signal processing and state estimation remains the Kalman filter (KF). The KF offers an optimal solution to the estimation problem under strict assumptions. Since this time, a number of other estimation strategies and filters were introduced to overcome robustness issues, such as the smooth variable structure filter (SVSF). In this paper, properties of the SVSF are explored in an effort to detect and diagnosis faults in an electromechanical system. The results are compared with the KF method, and future work is discussed.

  15. Fault diagnosis and fault-tolerant control based on adaptive control approach

    CERN Document Server

    Shen, Qikun; Shi, Peng

    2017-01-01

    This book provides recent theoretical developments in and practical applications of fault diagnosis and fault tolerant control for complex dynamical systems, including uncertain systems, linear and nonlinear systems. Combining adaptive control technique with other control methodologies, it investigates the problems of fault diagnosis and fault tolerant control for uncertain dynamic systems with or without time delay. As such, the book provides readers a solid understanding of fault diagnosis and fault tolerant control based on adaptive control technology. Given its depth and breadth, it is well suited for undergraduate and graduate courses on linear system theory, nonlinear system theory, fault diagnosis and fault tolerant control techniques. Further, it can be used as a reference source for academic research on fault diagnosis and fault tolerant control, and for postgraduates in the field of control theory and engineering. .

  16. Fault detection and isolation in GPS receiver autonomous integrity monitoring based on chaos particle swarm optimization-particle filter algorithm

    Science.gov (United States)

    Wang, Ershen; Jia, Chaoying; Tong, Gang; Qu, Pingping; Lan, Xiaoyu; Pang, Tao

    2018-03-01

    The receiver autonomous integrity monitoring (RAIM) is one of the most important parts in an avionic navigation system. Two problems need to be addressed to improve this system, namely, the degeneracy phenomenon and lack of samples for the standard particle filter (PF). However, the number of samples cannot adequately express the real distribution of the probability density function (i.e., sample impoverishment). This study presents a GPS receiver autonomous integrity monitoring (RAIM) method based on a chaos particle swarm optimization particle filter (CPSO-PF) algorithm with a log likelihood ratio. The chaos sequence generates a set of chaotic variables, which are mapped to the interval of optimization variables to improve particle quality. This chaos perturbation overcomes the potential for the search to become trapped in a local optimum in the particle swarm optimization (PSO) algorithm. Test statistics are configured based on a likelihood ratio, and satellite fault detection is then conducted by checking the consistency between the state estimate of the main PF and those of the auxiliary PFs. Based on GPS data, the experimental results demonstrate that the proposed algorithm can effectively detect and isolate satellite faults under conditions of non-Gaussian measurement noise. Moreover, the performance of the proposed novel method is better than that of RAIM based on the PF or PSO-PF algorithm.

  17. Filtering, control and fault detection with randomly occurring incomplete information

    CERN Document Server

    Dong, Hongli; Gao, Huijun

    2013-01-01

    This book investigates the filtering, control and fault detection problems for several classes of nonlinear systems with randomly occurring incomplete information. It proposes new concepts, including RVNs, ROMDs, ROMTCDs, and ROQEs. The incomplete information under consideration primarily includes missing measurements, time-delays, sensor and actuator saturations, quantization effects and time-varying nonlinearities. The first part of this book focuses on the filtering, control and fault detection problems for several classes of nonlinear stochastic discrete-time systems and

  18. A Novel Method for Detection and Classification of Covered Conductor Faults

    Directory of Open Access Journals (Sweden)

    Stanislav Misak

    2016-01-01

    Full Text Available Medium-Voltage (MV overhead lines with Covered Conductors (CCs are increasingly being used around the world primarily in forested or dissected terrain areas or in urban areas where it is not possible to utilize MV cable lines. The CC is specific in high operational reliability provided by the conductor core insulation compared to Aluminium-Conductor Steel-Reinforced (ACSR overhead lines. The only disadvantage of the CC is rather the problematic detection of faults compared to the ACSR. In this work, we consider the following faults: the contact of a tree branch with a CC and the fall of a conductor on the ground. The standard protection relays are unable to detect the faults and so the faults pose a risk for individuals in the vicinity of the conductor as well as it compromises the overall safety and reliability of the MV distribution system. In this article, we continue with our previous work aimed at the method enabling detection of the faults and we introduce a method enabling a classification of the fault type. Such a classification is especially important for an operator of an MV distribution system to plan the optimal maintenance or repair the faulty conductors since the fall of a tree branch can be solved later whereas the breakdown of a conductor means an immediate action of the operator.

  19. Faults in clays their detection and properties

    International Nuclear Information System (INIS)

    Baldi, G.; Carabelli, E.; Chiantore, V.; Colombo, P.F.; Gruszka, A.; Pensieri, R.; Superbo, S.; Gera, F.

    1991-01-01

    The 'Faults in clays project', a cooperative research effort between Ismes and Enea of Italy and BGS and Exeter University of the UK, has been aimed at assessing and improving the resolution capability of some high resolution geophysical techniques for the detection of discontinuities in clay formations. All Ismes activities have been carried out in Italy: they consisted in the search of one or more sites - faulted clay formations - suitable for the execution of geophysical and geotechnical investigations, in the execution of such tests and in additional geological surveys and laboratory (geotechnical and geochemical) testing. The selected sites were two quarries in plio-pleistocenic clay formations in central Italy where faults had been observed. The greatest part of the research work has been carried out in the Orte site where also two 90 m boreholes have been drilled and cored. Geophysical work at Orte consisted of vertical electrical soundings (VESs) and horizontal electrical lines (HELs), four high resolution seismic reflection lines, and in-hole and cross-hole logs. Laboratory activities were geotechnical characterization and permeability tests, and measurements of disequilibrium in the uranium decay series. At Narni, where Exeter University sampled soil gases for geochemical analyses, the geophysical work consisted in a geo-electrical survey (five VESs and two HELs), and in two high resolution reflection seismic lines. Additional investigations included a structural geology survey. The main conclusion of the research is that current geophysical techniques do not have a resolution capacity sufficient to detect the existence and determine the characteristics of faults in deep homogeneous clay formations

  20. Short-Circuit Fault Detection and Classification Using Empirical Wavelet Transform and Local Energy for Electric Transmission Line.

    Science.gov (United States)

    Huang, Nantian; Qi, Jiajin; Li, Fuqing; Yang, Dongfeng; Cai, Guowei; Huang, Guilin; Zheng, Jian; Li, Zhenxin

    2017-09-16

    In order to improve the classification accuracy of recognizing short-circuit faults in electric transmission lines, a novel detection and diagnosis method based on empirical wavelet transform (EWT) and local energy (LE) is proposed. First, EWT is used to deal with the original short-circuit fault signals from photoelectric voltage transformers, before the amplitude modulated-frequency modulated (AM-FM) mode with a compactly supported Fourier spectrum is extracted. Subsequently, the fault occurrence time is detected according to the modulus maxima of intrinsic mode function (IMF₂) from three-phase voltage signals processed by EWT. After this process, the feature vectors are constructed by calculating the LE of the fundamental frequency based on the three-phase voltage signals of one period after the fault occurred. Finally, the classifier based on support vector machine (SVM) which was constructed with the LE feature vectors is used to classify 10 types of short-circuit fault signals. Compared with complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and improved CEEMDAN methods, the new method using EWT has a better ability to present the frequency in time. The difference in the characteristics of the energy distribution in the time domain between different types of short-circuit faults can be presented by the feature vectors of LE. Together, simulation and real signals experiment demonstrate the validity and effectiveness of the new approach.

  1. A knowledge-based operator advisor system for integration of fault detection, control, and diagnosis to enhance the safe and reliable operation of nuclear power plants

    International Nuclear Information System (INIS)

    Bhatnagar, R.

    1989-01-01

    A Knowledged-Based Operator Advisor System has been developed for enhancing the complex task of maintaining safe and reliable operation of nuclear power plants. The operator's activities have been organized into the four tasks of data interpretation for abstracting high level information from sensor data, plant state monitoring for identification of faults, plan execution for controlling the faults, and diagnosis for determination of root causes of faults. The Operator Advisor System is capable of identifying the abnormal functioning of the plant in terms of: (1) deviations from normality, (2) pre-enumerated abnormal events, and (3) safety threats. The classification of abnormal functioning into the three categories of deviations from normality, abnormal events, and safety threats allows the detection of faults at three levels of: (1) developing faults, (2) developed faults, and (3) safety threatening faults. After the identification of abnormal functioning the system will identify the procedures to be executed to mitigate the consequences of abnormal functioning and will help the operator by displaying the procedure steps and monitoring the success of actions taken. The system also is capable of diagnosing the root causes of abnormal functioning. The identification, and diagnosis of root causes of abnormal functioning are done in parallel to the task of procedure execution, allowing the detection of more critical safety threats while executing procedures to control abnormal events

  2. Mobile crowdsourcing of data for fault detection and diagnosis in smart buildings

    DEFF Research Database (Denmark)

    Lazarova-Molnar, Sanja; Pór Logason, Halldór; Andersen, Peter Grønbæk

    2016-01-01

    Energy use of buildings represents roughly 40% of the overall energy consumption. Most of the national agendas contain goals related to reducing the energy consumption and carbon footprint. Timely and accurate fault detection and diagnosis (FDD) in building management systems (BMS) have...... the potential to reduce energy consumption cost by approximately 15-30%. Most of the FDD methods are data-based, meaning that their performance is tightly linked to the quality and availability of relevant data. Based on our experience, faults and relevant events data is very sparse and inadequate, mostly...... propose a strategy of how to successfully deploy this building occupants' crowdsourcing application. Copyright is held by the owner/author(s). Publication rights licensed to ACM....

  3. Efficient drilling problem detection. Early fault detection by the combination of physical models and artificial intelligence

    Energy Technology Data Exchange (ETDEWEB)

    Nyboe, Roar

    2009-09-15

    The drilling of an oil or gas well is an expensive undertaking. Hence, it is not surprising that mistakes and accidents during drilling incur a high cost. Accidents could result in the loss of expensive equipment and subsequent delays setting back the operation for days or weeks and thus running up large bills on rig-time and personnel hours. Some types of accidents also pose a risk to the personnel or the environment. In this dissertation we study alarm systems which could give the driller an early warning of upcoming problems, and thus provide time to avoid these accidents. We explore alarm systems which combine advanced physical models of the well and drilling process with artificial intelligence and time series analysis. Finally, we determine the advantages as well as the challenges of this approach. It is our hope that this dissertation is accessible to both practitioners in machine learning and control engineering, as well as to petroleum engineers with a passing familiarity with machine learning. Hence this dissertation starts with a quick introduction to drilling problems and some terms from time series analysis and machine learning. We then briefly describe the theory of observer-based fault detection and isolation. Theories of supervisory control systems are also introduced, as these concern both the choice of algorithms and how AI-based alarm systems integrate with the rest of the operation. From chapter 6 and onward, the challenges to fault detection in drilling are discussed. We focus on clarifying what restrictions the available training data put on our choice of machine learning methods. In chapter 8 and 9, we propose ways to combine machine learning and observer-based fault detection. Experimental results are presented in chapter 10, before we end with concluding remarks in chapter 11. Our main conclusion, reflected in our experimental results, is that physical models and artificial intelligence can be combined to produce hybrid alarm systems that

  4. Clustering of the Self-Organizing Map based Approach in Induction Machine Rotor Faults Diagnostics

    Directory of Open Access Journals (Sweden)

    Ahmed TOUMI

    2009-12-01

    Full Text Available Self-Organizing Maps (SOM is an excellent method of analyzingmultidimensional data. The SOM based classification is attractive, due to itsunsupervised learning and topology preserving properties. In this paper, theperformance of the self-organizing methods is investigated in induction motorrotor fault detection and severity evaluation. The SOM is based on motor currentsignature analysis (MCSA. The agglomerative hierarchical algorithms using theWard’s method is applied to automatically dividing the map into interestinginterpretable groups of map units that correspond to clusters in the input data. Theresults obtained with this approach make it possible to detect a rotor bar fault justdirectly from the visualization results. The system is also able to estimate theextent of rotor faults.

  5. A comparative study of sensor fault diagnosis methods based on observer for ECAS system

    Science.gov (United States)

    Xu, Xing; Wang, Wei; Zou, Nannan; Chen, Long; Cui, Xiaoli

    2017-03-01

    The performance and practicality of electronically controlled air suspension (ECAS) system are highly dependent on the state information supplied by kinds of sensors, but faults of sensors occur frequently. Based on a non-linearized 3-DOF 1/4 vehicle model, different methods of fault detection and isolation (FDI) are used to diagnose the sensor faults for ECAS system. The considered approaches include an extended Kalman filter (EKF) with concise algorithm, a strong tracking filter (STF) with robust tracking ability, and the cubature Kalman filter (CKF) with numerical precision. We propose three filters of EKF, STF, and CKF to design a state observer of ECAS system under typical sensor faults and noise. Results show that three approaches can successfully detect and isolate faults respectively despite of the existence of environmental noise, FDI time delay and fault sensitivity of different algorithms are different, meanwhile, compared with EKF and STF, CKF method has best performing FDI of sensor faults for ECAS system.

  6. Distributed bearing fault diagnosis based on vibration analysis

    Science.gov (United States)

    Dolenc, Boštjan; Boškoski, Pavle; Juričić, Đani

    2016-01-01

    Distributed bearing faults appear under various circumstances, for example due to electroerosion or the progression of localized faults. Bearings with distributed faults tend to generate more complex vibration patterns than those with localized faults. Despite the frequent occurrence of such faults, their diagnosis has attracted limited attention. This paper examines a method for the diagnosis of distributed bearing faults employing vibration analysis. The vibrational patterns generated are modeled by incorporating the geometrical imperfections of the bearing components. Comparing envelope spectra of vibration signals shows that one can distinguish between localized and distributed faults. Furthermore, a diagnostic procedure for the detection of distributed faults is proposed. This is evaluated on several bearings with naturally born distributed faults, which are compared with fault-free bearings and bearings with localized faults. It is shown experimentally that features extracted from vibrations in fault-free, localized and distributed fault conditions form clearly separable clusters, thus enabling diagnosis.

  7. A measurement-based technique for incipient anomaly detection

    KAUST Repository

    Harrou, Fouzi; Sun, Ying

    2016-01-01

    Fault detection is essential for safe operation of various engineering systems. Principal component analysis (PCA) has been widely used in monitoring highly correlated process variables. Conventional PCA-based methods, nevertheless, often fail to detect small or incipient faults. In this paper, we develop new PCA-based monitoring charts, combining PCA with multivariate memory control charts, such as the multivariate cumulative sum (MCUSUM) and multivariate exponentially weighted moving average (MEWMA) monitoring schemes. The multivariate control charts with memory are sensitive to small and moderate faults in the process mean, which significantly improves the performance of PCA methods and widen their applicability in practice. Using simulated data, we demonstrate that the proposed PCA-based MEWMA and MCUSUM control charts are more effective in detecting small shifts in the mean of the multivariate process variables, and outperform the conventional PCA-based monitoring charts. © 2015 IEEE.

  8. A measurement-based technique for incipient anomaly detection

    KAUST Repository

    Harrou, Fouzi

    2016-06-13

    Fault detection is essential for safe operation of various engineering systems. Principal component analysis (PCA) has been widely used in monitoring highly correlated process variables. Conventional PCA-based methods, nevertheless, often fail to detect small or incipient faults. In this paper, we develop new PCA-based monitoring charts, combining PCA with multivariate memory control charts, such as the multivariate cumulative sum (MCUSUM) and multivariate exponentially weighted moving average (MEWMA) monitoring schemes. The multivariate control charts with memory are sensitive to small and moderate faults in the process mean, which significantly improves the performance of PCA methods and widen their applicability in practice. Using simulated data, we demonstrate that the proposed PCA-based MEWMA and MCUSUM control charts are more effective in detecting small shifts in the mean of the multivariate process variables, and outperform the conventional PCA-based monitoring charts. © 2015 IEEE.

  9. Integrating physically based simulators with Event Detection Systems: Multi-site detection approach.

    Science.gov (United States)

    Housh, Mashor; Ohar, Ziv

    2017-03-01

    The Fault Detection (FD) Problem in control theory concerns of monitoring a system to identify when a fault has occurred. Two approaches can be distinguished for the FD: Signal processing based FD and Model-based FD. The former concerns of developing algorithms to directly infer faults from sensors' readings, while the latter uses a simulation model of the real-system to analyze the discrepancy between sensors' readings and expected values from the simulation model. Most contamination Event Detection Systems (EDSs) for water distribution systems have followed the signal processing based FD, which relies on analyzing the signals from monitoring stations independently of each other, rather than evaluating all stations simultaneously within an integrated network. In this study, we show that a model-based EDS which utilizes a physically based water quality and hydraulics simulation models, can outperform the signal processing based EDS. We also show that the model-based EDS can facilitate the development of a Multi-Site EDS (MSEDS), which analyzes the data from all the monitoring stations simultaneously within an integrated network. The advantage of the joint analysis in the MSEDS is expressed by increased detection accuracy (higher true positive alarms and fewer false alarms) and shorter detection time. Copyright © 2016 Elsevier Ltd. All rights reserved.

  10. Fault Detection of Wind Turbines with Uncertain Parameters: A Set-Membership Approach

    Directory of Open Access Journals (Sweden)

    Thomas Bak

    2012-07-01

    Full Text Available In this paper a set-membership approach for fault detection of a benchmark wind turbine is proposed. The benchmark represents relevant fault scenarios in the control system, including sensor, actuator and system faults. In addition we also consider parameter uncertainties and uncertainties on the torque coefficient. High noise on the wind speed measurement, nonlinearities in the aerodynamic torque and uncertainties on the parameters make fault detection a challenging problem. We use an effective wind speed estimator to reduce the noise on the wind speed measurements. A set-membership approach is used generate a set that contains all states consistent with the past measurements and the given model of the wind turbine including uncertainties and noise. This set represents all possible states the system can be in if not faulty. If the current measurement is not consistent with this set, a fault is detected. For representation of these sets we use zonotopes and for modeling of uncertainties we use matrix zonotopes, which yields a computationally efficient algorithm. The method is applied to the wind turbine benchmark problem without and with uncertainties. The result demonstrates the effectiveness of the proposed method compared to other proposed methods applied to the same problem. An advantage of the proposed method is that there is no need for threshold design, and it does not produce positive false alarms. In the case where uncertainty on the torque lookup table is introduced, some faults are not detectable. Previous research has not addressed this uncertainty. The method proposed here requires equal or less detection time than previous results.

  11. The New Method of the PV Panels Fault Detection Using Impedance Spectroscopy

    DEFF Research Database (Denmark)

    Symonowicz, Joanna Karolina; Riedel, Nicholas; Thorsteinsson, Sune

    The aim of our project is to develop a new method for photovoltaic (PV) panel fault detection based on analyzing impedance spectroscopy (IS) spectra. Although this technique was successful in assessing the state of degradation of fuel cells and batteries, it has never been applied to PV cells...

  12. Spectral negentropy based sidebands and demodulation analysis for planet bearing fault diagnosis

    Science.gov (United States)

    Feng, Zhipeng; Ma, Haoqun; Zuo, Ming J.

    2017-12-01

    Planet bearing vibration signals are highly complex due to intricate kinematics (involving both revolution and spinning) and strong multiple modulations (including not only the fault induced amplitude modulation and frequency modulation, but also additional amplitude modulations due to load zone passing, time-varying vibration transfer path, and time-varying angle between the gear pair mesh lines of action and fault impact force vector), leading to difficulty in fault feature extraction. Rolling element bearing fault diagnosis essentially relies on detection of fault induced repetitive impulses carried by resonance vibration, but they are usually contaminated by noise and therefor are hard to be detected. This further adds complexity to planet bearing diagnostics. Spectral negentropy is able to reveal the frequency distribution of repetitive transients, thus providing an approach to identify the optimal frequency band of a filter for separating repetitive impulses. In this paper, we find the informative frequency band (including the center frequency and bandwidth) of bearing fault induced repetitive impulses using the spectral negentropy based infogram. In Fourier spectrum, we identify planet bearing faults according to sideband characteristics around the center frequency. For demodulation analysis, we filter out the sensitive component based on the informative frequency band revealed by the infogram. In amplitude demodulated spectrum (squared envelope spectrum) of the sensitive component, we diagnose planet bearing faults by matching the present peaks with the theoretical fault characteristic frequencies. We further decompose the sensitive component into mono-component intrinsic mode functions (IMFs) to estimate their instantaneous frequencies, and select a sensitive IMF with an instantaneous frequency fluctuating around the center frequency for frequency demodulation analysis. In the frequency demodulated spectrum (Fourier spectrum of instantaneous frequency) of

  13. A hybrid robust fault tolerant control based on adaptive joint unscented Kalman filter.

    Science.gov (United States)

    Shabbouei Hagh, Yashar; Mohammadi Asl, Reza; Cocquempot, Vincent

    2017-01-01

    In this paper, a new hybrid robust fault tolerant control scheme is proposed. A robust H ∞ control law is used in non-faulty situation, while a Non-Singular Terminal Sliding Mode (NTSM) controller is activated as soon as an actuator fault is detected. Since a linear robust controller is designed, the system is first linearized through the feedback linearization method. To switch from one controller to the other, a fuzzy based switching system is used. An Adaptive Joint Unscented Kalman Filter (AJUKF) is used for fault detection and diagnosis. The proposed method is based on the simultaneous estimation of the system states and parameters. In order to show the efficiency of the proposed scheme, a simulated 3-DOF robotic manipulator is used. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  14. On-Line Fault Detection in Wind Turbine Transmission System using Adaptive Filter and Robust Statistical Features

    Directory of Open Access Journals (Sweden)

    Mark Frogley

    2013-01-01

    Full Text Available To reduce the maintenance cost, avoid catastrophic failure, and improve the wind transmission system reliability, online condition monitoring system is critical important. In the real applications, many rotating mechanical faults, such as bearing surface defect, gear tooth crack, chipped gear tooth and so on generate impulsive signals. When there are these types of faults developing inside rotating machinery, each time the rotating components pass over the damage point, an impact force could be generated. The impact force will cause a ringing of the support structure at the structural natural frequency. By effectively detecting those periodic impulse signals, one group of rotating machine faults could be detected and diagnosed. However, in real wind turbine operations, impulsive fault signals are usually relatively weak to the background noise and vibration signals generated from other healthy components, such as shaft, blades, gears and so on. Moreover, wind turbine transmission systems work under dynamic operating conditions. This will further increase the difficulties in fault detection and diagnostics. Therefore, developing advanced signal processing methods to enhance the impulsive signals is in great needs.In this paper, an adaptive filtering technique will be applied for enhancing the fault impulse signals-to-noise ratio in wind turbine gear transmission systems. Multiple statistical features designed to quantify the impulsive signals of the processed signal are extracted for bearing fault detection. The multiple dimensional features are then transformed into one dimensional feature. A minimum error rate classifier will be designed based on the compressed feature to identify the gear transmission system with defect. Real wind turbine vibration signals will be used to demonstrate the effectiveness of the presented methodology.

  15. Set-membership fault detection under noisy environment with application to the detection of abnormal aircraft control surface positions

    Science.gov (United States)

    El Houda Thabet, Rihab; Combastel, Christophe; Raïssi, Tarek; Zolghadri, Ali

    2015-09-01

    The paper develops a set membership detection methodology which is applied to the detection of abnormal positions of aircraft control surfaces. Robust and early detection of such abnormal positions is an important issue for early system reconfiguration and overall optimisation of aircraft design. In order to improve fault sensitivity while ensuring a high level of robustness, the method combines a data-driven characterisation of noise and a model-driven approach based on interval prediction. The efficiency of the proposed methodology is illustrated through simulation results obtained based on data recorded in several flight scenarios of a highly representative aircraft benchmark.

  16. Early fault detection and on-line diagnosis in real-time environments

    Directory of Open Access Journals (Sweden)

    Andreas Bye

    1993-01-01

    Full Text Available This paper describes an approach to fault detection and diagnosis involving the simultaneous employment of quantitative and qualitative reasoning techniques. We show that early identification of process anomalies by means of a separate fault detection module paves the way for a fast and accuratc follow-up diagnosis. The diagnosis task is dramatically simplified because the diagnostic inferences can be performed at the soonest possible time: when the detection module first spots deviations between its calculated reference points and the corresponding measurements from the process.

  17. Implementation and testing of a fault detection software tool for improving control system performance in a large commercial building

    Energy Technology Data Exchange (ETDEWEB)

    Salsbury, T.I.; Diamond, R.C.

    2000-05-01

    This paper describes a model-based, feedforward control scheme that can detect faults in the controlled process and improve control performance over traditional PID control. The tool uses static simulation models of the system under control to generate feed-forward control action, which acts as a reference of correct operation. Faults that occur in the system cause discrepancies between the feedforward models and the controlled process. The scheme facilitates detection of faults by monitoring the level of these discrepancies. We present results from the first phase of tests on a dual-duct air-handling unit installed in a large office building in San Francisco. We demonstrate the ability of the tool to detect a number of preexisting faults in the system and discuss practical issues related to implementation.

  18. Fault diagnosis for wind turbine planetary ring gear via a meshing resonance based filtering algorithm.

    Science.gov (United States)

    Wang, Tianyang; Chu, Fulei; Han, Qinkai

    2017-03-01

    Identifying the differences between the spectra or envelope spectra of a faulty signal and a healthy baseline signal is an efficient planetary gearbox local fault detection strategy. However, causes other than local faults can also generate the characteristic frequency of a ring gear fault; this may further affect the detection of a local fault. To address this issue, a new filtering algorithm based on the meshing resonance phenomenon is proposed. In detail, the raw signal is first decomposed into different frequency bands and levels. Then, a new meshing index and an MRgram are constructed to determine which bands belong to the meshing resonance frequency band. Furthermore, an optimal filter band is selected from this MRgram. Finally, the ring gear fault can be detected according to the envelope spectrum of the band-pass filtering result. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  19. DYNAMIC SOFTWARE TESTING MODELS WITH PROBABILISTIC PARAMETERS FOR FAULT DETECTION AND ERLANG DISTRIBUTION FOR FAULT RESOLUTION DURATION

    Directory of Open Access Journals (Sweden)

    A. D. Khomonenko

    2016-07-01

    Full Text Available Subject of Research.Software reliability and test planning models are studied taking into account the probabilistic nature of error detection and discovering. Modeling of software testing enables to plan the resources and final quality at early stages of project execution. Methods. Two dynamic models of processes (strategies are suggested for software testing, using error detection probability for each software module. The Erlang distribution is used for arbitrary distribution approximation of fault resolution duration. The exponential distribution is used for approximation of fault resolution discovering. For each strategy, modified labeled graphs are built, along with differential equation systems and their numerical solutions. The latter makes it possible to compute probabilistic characteristics of the test processes and states: probability states, distribution functions for fault detection and elimination, mathematical expectations of random variables, amount of detected or fixed errors. Evaluation of Results. Probabilistic characteristics for software development projects were calculated using suggested models. The strategies have been compared by their quality indexes. Required debugging time to achieve the specified quality goals was calculated. The calculation results are used for time and resources planning for new projects. Practical Relevance. The proposed models give the possibility to use the reliability estimates for each individual module. The Erlang approximation removes restrictions on the use of arbitrary time distribution for fault resolution duration. It improves the accuracy of software test process modeling and helps to take into account the viability (power of the tests. With the use of these models we can search for ways to improve software reliability by generating tests which detect errors with the highest probability.

  20. All row, planar fault detection system

    Science.gov (United States)

    Archer, Charles Jens; Pinnow, Kurt Walter; Ratterman, Joseph D; Smith, Brian Edward

    2013-07-23

    An apparatus, program product and method for detecting nodal faults may simultaneously cause designated nodes of a cell to communicate with all nodes adjacent to each of the designated nodes. Furthermore, all nodes along the axes of the designated nodes are made to communicate with their adjacent nodes, and the communications are analyzed to determine if a node or connection is faulty.

  1. Monitoring and diagnosis for sensor fault detection using GMDH methodology

    International Nuclear Information System (INIS)

    Goncalves, Iraci Martinez Pereira

    2006-01-01

    The fault detection and diagnosis system is an Operator Support System dedicated to specific functions that alerts operators to sensors and actuators fault problems, and guide them in the diagnosis before the normal alarm limits are reached. Operator Support Systems appears to reduce panels complexity caused by the increase of the available information in nuclear power plants control room. In this work a Monitoring and Diagnosis System was developed based on the GMDH (Group Method of Data Handling) methodology. The methodology was applied to the IPEN research reactor IEA-R1. The system performs the monitoring, comparing GMDH model calculated values with measured values. The methodology developed was firstly applied in theoretical models: a heat exchanger model and an IPEN reactor theoretical model. The results obtained with theoretical models gave a base to methodology application to the actual reactor operation data. Three GMDH models were developed for actual operation data monitoring: the first one using just the thermal process variables, the second one was developed considering also some nuclear variables, and the third GMDH model considered all the reactor variables. The three models presented excellent results, showing the methodology utilization viability in monitoring the operation data. The comparison between the three developed models results also shows the methodology capacity to choose by itself the best set of input variables for the model optimization. For the system diagnosis implementation, faults were simulated in the actual temperature variable values by adding a step change. The fault values correspond to a typical temperature descalibration and the result of monitoring faulty data was then used to build a simple diagnosis system based on fuzzy logic. (author)

  2. A Model of Intelligent Fault Diagnosis of Power Equipment Based on CBR

    Directory of Open Access Journals (Sweden)

    Gang Ma

    2015-01-01

    Full Text Available Nowadays the demand of power supply reliability has been strongly increased as the development within power industry grows rapidly. Nevertheless such large demand requires substantial power grid to sustain. Therefore power equipment’s running and testing data which contains vast information underpins online monitoring and fault diagnosis to finally achieve state maintenance. In this paper, an intelligent fault diagnosis model for power equipment based on case-based reasoning (IFDCBR will be proposed. The model intends to discover the potential rules of equipment fault by data mining. The intelligent model constructs a condition case base of equipment by analyzing the following four categories of data: online recording data, history data, basic test data, and environmental data. SVM regression analysis was also applied in mining the case base so as to further establish the equipment condition fingerprint. The running data of equipment can be diagnosed by such condition fingerprint to detect whether there is a fault or not. Finally, this paper verifies the intelligent model and three-ratio method based on a set of practical data. The resulting research demonstrates that this intelligent model is more effective and accurate in fault diagnosis.

  3. Fault detection and diagnosis for compliance monitoring in international supply chains

    NARCIS (Netherlands)

    Wang, Yuxin; Tian, Yifu; Teixeira, André; Hulstijn, Joris; Tan, Yao-Hua

    Currently international supply chains are facing risks concerning faults in compliance, such as altering shipping documentations, fictitious inventory, and inter-company manipulations. In this paper a method to detect and diagnose fault scenarios regarding customs compliance in supply chains is

  4. Fault diagnosis of downhole drilling incidents using adaptive observers and statistical change detection

    DEFF Research Database (Denmark)

    Willersrud, Anders; Blanke, Mogens; Imsland, Lars

    2015-01-01

    Downhole abnormal incidents during oil and gas drilling causes costly delays, any may also potentially lead to dangerous scenarios. Dierent incidents willcause changes to dierent parts of the physics of the process. Estimating thechanges in physical parameters, and correlating these with changes ...... expectedfrom various defects, can be used to diagnose faults while in development.This paper shows how estimated friction parameters and ow rates can de-tect and isolate the type of incident, as well as isolating the position of adefect. Estimates are shown to be subjected to non......-Gaussian,t-distributednoise, and a dedicated multivariate statistical change detection approach isused that detects and isolates faults by detecting simultaneous changes inestimated parameters and ow rates. The properties of the multivariate di-agnosis method are analyzed, and it is shown how detection and false alarmprobabilities...... are assessed and optimized using data-based learning to obtainthresholds for hypothesis testing. Data from a 1400 m horizontal ow loop isused to test the method, and successful diagnosis of the incidents drillstringwashout (pipe leakage), lost circulation, gas in ux, and drill bit plugging aredemonstrated....

  5. Navigation System Fault Diagnosis for Underwater Vehicle

    DEFF Research Database (Denmark)

    Falkenberg, Thomas; Gregersen, Rene Tavs; Blanke, Mogens

    2014-01-01

    This paper demonstrates fault diagnosis on unmanned underwater vehicles (UUV) based on analysis of structure of the nonlinear dynamics. Residuals are generated using dierent approaches in structural analysis followed by statistical change detection. Hypothesis testing thresholds are made signal...... based to cope with non-ideal properties seen in real data. Detection of both sensor and thruster failures are demonstrated. Isolation is performed using the residual signature of detected faults and the change detection algorithm is used to assess severity of faults by estimating their magnitude...

  6. Data-based fault-tolerant control for affine nonlinear systems with actuator faults.

    Science.gov (United States)

    Xie, Chun-Hua; Yang, Guang-Hong

    2016-09-01

    This paper investigates the fault-tolerant control (FTC) problem for unknown nonlinear systems with actuator faults including stuck, outage, bias and loss of effectiveness. The upper bounds of stuck faults, bias faults and loss of effectiveness faults are unknown. A new data-based FTC scheme is proposed. It consists of the online estimations of the bounds and a state-dependent function. The estimations are adjusted online to compensate automatically the actuator faults. The state-dependent function solved by using real system data helps to stabilize the system. Furthermore, all signals in the resulting closed-loop system are uniformly bounded and the states converge asymptotically to zero. Compared with the existing results, the proposed approach is data-based. Finally, two simulation examples are provided to show the effectiveness of the proposed approach. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  7. Statistical Feature Extraction for Fault Locations in Nonintrusive Fault Detection of Low Voltage Distribution Systems

    Directory of Open Access Journals (Sweden)

    Hsueh-Hsien Chang

    2017-04-01

    Full Text Available This paper proposes statistical feature extraction methods combined with artificial intelligence (AI approaches for fault locations in non-intrusive single-line-to-ground fault (SLGF detection of low voltage distribution systems. The input features of the AI algorithms are extracted using statistical moment transformation for reducing the dimensions of the power signature inputs measured by using non-intrusive fault monitoring (NIFM techniques. The data required to develop the network are generated by simulating SLGF using the Electromagnetic Transient Program (EMTP in a test system. To enhance the identification accuracy, these features after normalization are given to AI algorithms for presenting and evaluating in this paper. Different AI techniques are then utilized to compare which identification algorithms are suitable to diagnose the SLGF for various power signatures in a NIFM system. The simulation results show that the proposed method is effective and can identify the fault locations by using non-intrusive monitoring techniques for low voltage distribution systems.

  8. Multi-Level Wavelet Shannon Entropy-Based Method for Single-Sensor Fault Location

    Directory of Open Access Journals (Sweden)

    Qiaoning Yang

    2015-10-01

    Full Text Available In actual application, sensors are prone to failure because of harsh environments, battery drain, and sensor aging. Sensor fault location is an important step for follow-up sensor fault detection. In this paper, two new multi-level wavelet Shannon entropies (multi-level wavelet time Shannon entropy and multi-level wavelet time-energy Shannon entropy are defined. They take full advantage of sensor fault frequency distribution and energy distribution across multi-subband in wavelet domain. Based on the multi-level wavelet Shannon entropy, a method is proposed for single sensor fault location. The method firstly uses a criterion of maximum energy-to-Shannon entropy ratio to select the appropriate wavelet base for signal analysis. Then multi-level wavelet time Shannon entropy and multi-level wavelet time-energy Shannon entropy are used to locate the fault. The method is validated using practical chemical gas concentration data from a gas sensor array. Compared with wavelet time Shannon entropy and wavelet energy Shannon entropy, the experimental results demonstrate that the proposed method can achieve accurate location of a single sensor fault and has good anti-noise ability. The proposed method is feasible and effective for single-sensor fault location.

  9. Development Ground Fault Detecting System for D.C Voltage Line

    Energy Technology Data Exchange (ETDEWEB)

    Kim Taek Soo; Song Ung Il; Gwon, Young Dong; Lee Hyoung Kee [Korea Electric Power Research Institute, Taejon (Korea, Republic of)

    1996-12-31

    It is necessary to keep the security of reliability and to maximize the efficiency of maintenance by prompt detection of a D.C feeder ground fault point at the built ed or a building power plants. At present, the most of the power plants are set up the ground fault indicator lamp in the monitor room. If a ground fault occurs on DC voltage feeder, a current through the ground fault relay is adjusted and the lamps have brightened while the current flows the relay coil. In order to develop such a system, it is analyzed a D.C feeder ground circuit theoretically and studied a principles which can determine ground fault point or a polarity discrimination and a phase discrimination of the line. So, the developed system through this principles can compute a resistance ground fault current and a capacitive ground fault current. It shows that the system can defect a ground fault point or a bad insulated line by measuring a power plant D.C feeder insulation resistance at the un interruptible power status, and therefore the power plant could protect an unexpected service interruption . (author). 18 refs., figs.

  10. Fault diagnosis for tilting-pad journal bearing based on SVD and LMD

    Directory of Open Access Journals (Sweden)

    Zhang Xiaotao

    2016-01-01

    Full Text Available Aiming at fault diagnosis for tilting-pad journal bearing with fluid support developed recently, a new method based on singular value decomposition (SVD and local mean decomposition (LMD is proposed. First, the phase space reconstruction of Hankel matrix and SVD method are used as pre-filter process unit to reduce the random noises in the original signal. Then the purified signal is decomposed by LMD into a series of production functions (PFs. Based on PFs, time frequency map and marginal spectrum can be obtained for fault diagnosis. Finally, this method is applied to numerical simulation and practical experiment data. The results show that the proposed method can effectively detect fault features of tilting-pad journal bearing.

  11. Enhanced dynamic data-driven fault detection approach: Application to a two-tank heater system

    KAUST Repository

    Harrou, Fouzi; Madakyaru, Muddu; Sun, Ying; Kammammettu, Sanjula

    2018-01-01

    on PCA approach a challenging task. Accounting for the dynamic nature of data can also reflect the performance of the designed fault detection approaches. In PCA-based methods, this dynamic characteristic of the data can be accounted for by using dynamic

  12. Traction Inverter Open Switch Fault Diagnosis Based on Choi–Williams Distribution Spectral Kurtosis and Wavelet-Packet Energy Shannon Entropy

    Directory of Open Access Journals (Sweden)

    Shuangshuang Lin

    2017-09-01

    Full Text Available In this paper, a new approach for fault detection and location of open switch faults in the closed-loop inverter fed vector controlled drives of Electric Multiple Units is proposed. Spectral kurtosis (SK based on Choi–Williams distribution (CWD as a statistical tool can effectively indicate the presence of transients and locations in the frequency domain. Wavelet-packet energy Shannon entropy (WPESE is appropriate for the transient changes detection of complex non-linear and non-stationary signals. Based on the analyses of currents in normal and fault conditions, SK based on CWD and WPESE are combined with the DC component method. SK based on CWD and WPESE are used for the fault detection, and the DC component method is used for the fault localization. This approach can diagnose the specific locations of faulty Insulated Gate Bipolar Transistors (IGBTs with high accuracy, and it requires no additional devices. Experiments on the RT-LAB platform are carried out and the experimental results verify the feasibility and effectiveness of the diagnosis method.

  13. Verification of a Novel Method of Detecting Faults in Medium-Voltage Systems with Covered Conductors

    Directory of Open Access Journals (Sweden)

    Mišák Stanislav

    2017-06-01

    Full Text Available This paper describes the use of new methods of detecting faults in medium-voltage overhead lines built of covered conductors. The methods mainly address such faults as falling of a conductor, contacting a conductor with a tree branch, or falling a tree branch across three phases of a medium-voltage conductor. These faults cannot be detected by current digital relay protection systems. Therefore, a new system that can detect the above mentioned faults was developed. After having tested its operation, the system has already been implemented to protect mediumvoltage overhead lines built of covered conductors.

  14. Ontology-Based Method for Fault Diagnosis of Loaders.

    Science.gov (United States)

    Xu, Feixiang; Liu, Xinhui; Chen, Wei; Zhou, Chen; Cao, Bingwei

    2018-02-28

    This paper proposes an ontology-based fault diagnosis method which overcomes the difficulty of understanding complex fault diagnosis knowledge of loaders and offers a universal approach for fault diagnosis of all loaders. This method contains the following components: (1) An ontology-based fault diagnosis model is proposed to achieve the integrating, sharing and reusing of fault diagnosis knowledge for loaders; (2) combined with ontology, CBR (case-based reasoning) is introduced to realize effective and accurate fault diagnoses following four steps (feature selection, case-retrieval, case-matching and case-updating); and (3) in order to cover the shortages of the CBR method due to the lack of concerned cases, ontology based RBR (rule-based reasoning) is put forward through building SWRL (Semantic Web Rule Language) rules. An application program is also developed to implement the above methods to assist in finding the fault causes, fault locations and maintenance measures of loaders. In addition, the program is validated through analyzing a case study.

  15. Wayside Bearing Fault Diagnosis Based on a Data-Driven Doppler Effect Eliminator and Transient Model Analysis

    Science.gov (United States)

    Liu, Fang; Shen, Changqing; He, Qingbo; Zhang, Ao; Liu, Yongbin; Kong, Fanrang

    2014-01-01

    A fault diagnosis strategy based on the wayside acoustic monitoring technique is investigated for locomotive bearing fault diagnosis. Inspired by the transient modeling analysis method based on correlation filtering analysis, a so-called Parametric-Mother-Doppler-Wavelet (PMDW) is constructed with six parameters, including a center characteristic frequency and five kinematic model parameters. A Doppler effect eliminator containing a PMDW generator, a correlation filtering analysis module, and a signal resampler is invented to eliminate the Doppler effect embedded in the acoustic signal of the recorded bearing. Through the Doppler effect eliminator, the five kinematic model parameters can be identified based on the signal itself. Then, the signal resampler is applied to eliminate the Doppler effect using the identified parameters. With the ability to detect early bearing faults, the transient model analysis method is employed to detect localized bearing faults after the embedded Doppler effect is eliminated. The effectiveness of the proposed fault diagnosis strategy is verified via simulation studies and applications to diagnose locomotive roller bearing defects. PMID:24803197

  16. Compressive Sensing of Roller Bearing Faults via Harmonic Detection from Under-Sampled Vibration Signals.

    Science.gov (United States)

    Tang, Gang; Hou, Wei; Wang, Huaqing; Luo, Ganggang; Ma, Jianwei

    2015-10-09

    The Shannon sampling principle requires substantial amounts of data to ensure the accuracy of on-line monitoring of roller bearing fault signals. Challenges are often encountered as a result of the cumbersome data monitoring, thus a novel method focused on compressed vibration signals for detecting roller bearing faults is developed in this study. Considering that harmonics often represent the fault characteristic frequencies in vibration signals, a compressive sensing frame of characteristic harmonics is proposed to detect bearing faults. A compressed vibration signal is first acquired from a sensing matrix with information preserved through a well-designed sampling strategy. A reconstruction process of the under-sampled vibration signal is then pursued as attempts are conducted to detect the characteristic harmonics from sparse measurements through a compressive matching pursuit strategy. In the proposed method bearing fault features depend on the existence of characteristic harmonics, as typically detected directly from compressed data far before reconstruction completion. The process of sampling and detection may then be performed simultaneously without complete recovery of the under-sampled signals. The effectiveness of the proposed method is validated by simulations and experiments.

  17. Process for detecting leak faults using a helium mass spectrometer

    International Nuclear Information System (INIS)

    Divet, Claude; Morin, Claude.

    1977-01-01

    The description is given of a process for detecting very small leak faults putting into communication the outer and inner sides of the wall of a containment, one of these wall sides being in contact with gaseous helium under a pressure of around one torr, the other side being one of the limits of a space pumped down to a residual gas pressure under 10 -3 torr. This space is in communication with the measuring cell of a helium mass spectrometer. This process may be applied to the detection of faults in metal claddings of the fuel rods used in nuclear reactors [fr

  18. An application of LTR design in fault detection

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik

    1998-01-01

    The fault detection and isolation (FDI) problem is considered in this paper. The FDI problem is formulated as a filter design problem, where the faults in the system is estimated and the disturbance acting on the system is rejected. It turns out that the filter design problem can be considered...... as a standard Loop Transfer Recovery (LTR) design problem. As a consequence of the connection between LTR and FDI design, it is shown in an example how the LQG/LTR design method for full order and a proportional-integral observer can be applied with advantages in connection with FDI....

  19. Detection of Two-Level Inverter Open-Circuit Fault Using a Combined DWT-NN Approach

    Directory of Open Access Journals (Sweden)

    Bilal Djamel Eddine Cherif

    2018-01-01

    Full Text Available Three-phase static converters with voltage structure are widely used in many industrial systems. In order to prevent the propagation of the fault to other components of the system and ensure continuity of service in the event of a failure of the converter, efficient and rapid methods of detection and localization must be implemented. This paper work addresses a diagnostic technique based on the discrete wavelet transform (DWT algorithm and the approach of neural network (NN, for the detection of an inverter IGBT open-circuit switch fault. To illustrate the merits of the technique and validate the results, experimental tests are conducted using a built voltage inverter fed induction motor. The inverter is controlled by the SVM control strategy.

  20. Fault Detection and Diagnosis for Gas Turbines Based on a Kernelized Information Entropy Model

    Directory of Open Access Journals (Sweden)

    Weiying Wang

    2014-01-01

    Full Text Available Gas turbines are considered as one kind of the most important devices in power engineering and have been widely used in power generation, airplanes, and naval ships and also in oil drilling platforms. However, they are monitored without man on duty in the most cases. It is highly desirable to develop techniques and systems to remotely monitor their conditions and analyze their faults. In this work, we introduce a remote system for online condition monitoring and fault diagnosis of gas turbine on offshore oil well drilling platforms based on a kernelized information entropy model. Shannon information entropy is generalized for measuring the uniformity of exhaust temperatures, which reflect the overall states of the gas paths of gas turbine. In addition, we also extend the entropy to compute the information quantity of features in kernel spaces, which help to select the informative features for a certain recognition task. Finally, we introduce the information entropy based decision tree algorithm to extract rules from fault samples. The experiments on some real-world data show the effectiveness of the proposed algorithms.

  1. Fault detection and diagnosis for gas turbines based on a kernelized information entropy model.

    Science.gov (United States)

    Wang, Weiying; Xu, Zhiqiang; Tang, Rui; Li, Shuying; Wu, Wei

    2014-01-01

    Gas turbines are considered as one kind of the most important devices in power engineering and have been widely used in power generation, airplanes, and naval ships and also in oil drilling platforms. However, they are monitored without man on duty in the most cases. It is highly desirable to develop techniques and systems to remotely monitor their conditions and analyze their faults. In this work, we introduce a remote system for online condition monitoring and fault diagnosis of gas turbine on offshore oil well drilling platforms based on a kernelized information entropy model. Shannon information entropy is generalized for measuring the uniformity of exhaust temperatures, which reflect the overall states of the gas paths of gas turbine. In addition, we also extend the entropy to compute the information quantity of features in kernel spaces, which help to select the informative features for a certain recognition task. Finally, we introduce the information entropy based decision tree algorithm to extract rules from fault samples. The experiments on some real-world data show the effectiveness of the proposed algorithms.

  2. Fault Diagnosis of a Reconfigurable Crawling–Rolling Robot Based on Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Karthikeyan Elangovan

    2017-10-01

    Full Text Available As robots begin to perform jobs autonomously, with minimal or no human intervention, a new challenge arises: robots also need to autonomously detect errors and recover from faults. In this paper, we present a Support Vector Machine (SVM-based fault diagnosis system for a bio-inspired reconfigurable robot named Scorpio. The diagnosis system needs to detect and classify faults while Scorpio uses its crawling and rolling locomotion modes. Specifically, we classify between faulty and non-faulty conditions by analyzing onboard Inertial Measurement Unit (IMU sensor data. The data capture nine different locomotion gaits, which include rolling and crawling modes, at three different speeds. Statistical methods are applied to extract features and to reduce the dimensionality of original IMU sensor data features. These statistical features were given as inputs for training and testing. Additionally, the c-Support Vector Classification (c-SVC and nu-SVC models of SVM, and their fault classification accuracies, were compared. The results show that the proposed SVM approach can be used to autonomously diagnose locomotion gait faults while the reconfigurable robot is in operation.

  3. Fault-tolerant cooperative output regulation for multi-vehicle systems with sensor faults

    Science.gov (United States)

    Qin, Liguo; He, Xiao; Zhou, D. H.

    2017-10-01

    This paper presents a unified framework of fault diagnosis and fault-tolerant cooperative output regulation (FTCOR) for a linear discrete-time multi-vehicle system with sensor faults. The FTCOR control law is designed through three steps. A cooperative output regulation (COR) controller is designed based on the internal mode principle when there are no sensor faults. A sufficient condition on the existence of the COR controller is given based on the discrete-time algebraic Riccati equation (DARE). Then, a decentralised fault diagnosis scheme is designed to cope with sensor faults occurring in followers. A residual generator is developed to detect sensor faults of each follower, and a bank of fault-matching estimators are proposed to isolate and estimate sensor faults of each follower. Unlike the current distributed fault diagnosis for multi-vehicle systems, the presented decentralised fault diagnosis scheme in each vehicle reduces the communication and computation load by only using the information of the vehicle. By combing the sensor fault estimation and the COR control law, an FTCOR controller is proposed. Finally, the simulation results demonstrate the effectiveness of the FTCOR controller.

  4. Protection algorithm for a wind turbine generator based on positive- and negative-sequence fault components

    DEFF Research Database (Denmark)

    Zheng, Tai-Ying; Cha, Seung-Tae; Crossley, Peter A.

    2011-01-01

    A protection relay for a wind turbine generator (WTG) based on positive- and negative-sequence fault components is proposed in the paper. The relay uses the magnitude of the positive-sequence component in the fault current to detect a fault on a parallel WTG, connected to the same power collection...... feeder, or a fault on an adjacent feeder; but for these faults, the relay remains stable and inoperative. A fault on the power collection feeder or a fault on the collection bus, both of which require an instantaneous tripping response, are distinguished from an inter-tie fault or a grid fault, which...... in the fault current is used to decide on either instantaneous or delayed operation. The operating performance of the relay is then verified using various fault scenarios modelled using EMTP-RV. The scenarios involve changes in the position and type of fault, and the faulted phases. Results confirm...

  5. CRISP. Fault detection, analysis and diagnostics in high-DG distribution systems

    International Nuclear Information System (INIS)

    Fontela, M.; Bacha, S.; Hadsjaid, N.; Andrieu, C.; Raison, B.; Penkov, D.

    2004-04-01

    The fault in the electrotechnical meaning is defined in the document. The main part of faults in overhead lines are non permanent faults, what entails the network operator to maintain the existing techniques to clear as fast as possible these faults. When a permanent fault occurs the operator has to detect and to limit the risks as soon as possible. Different axes are followed: limitation of the fault current, clearing the faulted feeder, locating the fault by test and try under possible fault condition. So the fault detection, fault clearing and fault localization are important functions of an EPS (electric power systems) to allow secure and safe operation of the system. The function may be improved by means of a better use of ICT components in the future sharing conveniently the intelligence needed near the distributed devices and a defined centralized intelligence. This improvement becomes necessary in distribution EPS with a high introduction of DR (distributed resources). The transmission and sub-transmission protection systems are already installed in order to manage power flow in all directions, so the DR issue is less critical for this part of the power system in term of fault clearing and diagnosis. Nevertheless the massive introduction of RES involves another constraints to the transmission system which are the bottlenecks caused by important local and fast installed production as wind power plants. Dealing with the distribution power system, and facing a permanent fault, two main actions must be achieved: identify the faulted elementary EPS area quickly and allow the field crew to locate and to repair the fault as soon as possible. The introduction of DR in distribution EPS involves some changes in fault location methods or equipment. The different existing neutral grounding systems make it difficult the achievement of a general method relevant for any distribution EPS in Europe. Some solutions are studied in the CRISP project in order to improve the

  6. Compressed sensing of roller bearing fault based on multiple down-sampling strategy

    Science.gov (United States)

    Wang, Huaqing; Ke, Yanliang; Luo, Ganggang; Tang, Gang

    2016-02-01

    Roller bearings are essential components of rotating machinery and are often exposed to complex operating conditions, which can easily lead to their failures. Thus, to ensure normal production and the safety of machine operators, it is essential to detect the failures as soon as possible. However, it is a major challenge to maintain a balance between detection efficiency and big data acquisition given the limitations of sampling theory. To overcome these limitations, we try to preserve the information pertaining to roller bearing failures using a sampling rate far below the Nyquist sampling rate, which can ease the pressure generated by the large-scale data. The big data of a faulty roller bearing’s vibration signals is firstly reduced by a down-sample strategy while preserving the fault features by selecting peaks to represent the data segments in time domain. However, a problem arises in that the fault features may be weaker than before, since the noise may be mistaken for the peaks when the noise is stronger than the vibration signals, which makes the fault features unable to be extracted by commonly-used envelope analysis. Here we employ compressive sensing theory to overcome this problem, which can make a signal enhancement and reduce the sample sizes further. Moreover, it is capable of detecting fault features from a small number of samples based on orthogonal matching pursuit approach, which can overcome the shortcomings of the multiple down-sample algorithm. Experimental results validate the effectiveness of the proposed technique in detecting roller bearing faults.

  7. Compressed sensing of roller bearing fault based on multiple down-sampling strategy

    International Nuclear Information System (INIS)

    Wang, Huaqing; Ke, Yanliang; Luo, Ganggang; Tang, Gang

    2016-01-01

    Roller bearings are essential components of rotating machinery and are often exposed to complex operating conditions, which can easily lead to their failures. Thus, to ensure normal production and the safety of machine operators, it is essential to detect the failures as soon as possible. However, it is a major challenge to maintain a balance between detection efficiency and big data acquisition given the limitations of sampling theory. To overcome these limitations, we try to preserve the information pertaining to roller bearing failures using a sampling rate far below the Nyquist sampling rate, which can ease the pressure generated by the large-scale data. The big data of a faulty roller bearing’s vibration signals is firstly reduced by a down-sample strategy while preserving the fault features by selecting peaks to represent the data segments in time domain. However, a problem arises in that the fault features may be weaker than before, since the noise may be mistaken for the peaks when the noise is stronger than the vibration signals, which makes the fault features unable to be extracted by commonly-used envelope analysis. Here we employ compressive sensing theory to overcome this problem, which can make a signal enhancement and reduce the sample sizes further. Moreover, it is capable of detecting fault features from a small number of samples based on orthogonal matching pursuit approach, which can overcome the shortcomings of the multiple down-sample algorithm. Experimental results validate the effectiveness of the proposed technique in detecting roller bearing faults. (paper)

  8. Robust fault detection for the dynamics of high-speed train with multi-source finite frequency interference.

    Science.gov (United States)

    Bai, Weiqi; Dong, Hairong; Yao, Xiuming; Ning, Bin

    2018-04-01

    This paper proposes a composite fault detection scheme for the dynamics of high-speed train (HST), using an unknown input observer-like (UIO-like) fault detection filter, in the presence of wind gust and operating noises which are modeled as disturbance generated by exogenous system and unknown multi-source disturbance within finite frequency domain. Using system input and system output measurements, the fault detection filter is designed to generate the needed residual signals. In order to decouple disturbance from residual signals without truncating the influence of faults, this paper proposes a method to partition the disturbance into two parts. One subset of the disturbance does not appear in residual dynamics, and the influence of the other subset is constrained by H ∞ performance index in a finite frequency domain. A set of detection subspaces are defined, and every different fault is assigned to its own detection subspace to guarantee the residual signals are diagonally affected promptly by the faults. Simulations are conducted to demonstrate the effectiveness and merits of the proposed method. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

  9. PLAT: An Automated Fault and Behavioural Anomaly Detection Tool for PLC Controlled Manufacturing Systems.

    Science.gov (United States)

    Ghosh, Arup; Qin, Shiming; Lee, Jooyeoun; Wang, Gi-Nam

    2016-01-01

    Operational faults and behavioural anomalies associated with PLC control processes take place often in a manufacturing system. Real time identification of these operational faults and behavioural anomalies is necessary in the manufacturing industry. In this paper, we present an automated tool, called PLC Log-Data Analysis Tool (PLAT) that can detect them by using log-data records of the PLC signals. PLAT automatically creates a nominal model of the PLC control process and employs a novel hash table based indexing and searching scheme to satisfy those purposes. Our experiments show that PLAT is significantly fast, provides real time identification of operational faults and behavioural anomalies, and can execute within a small memory footprint. In addition, PLAT can easily handle a large manufacturing system with a reasonable computing configuration and can be installed in parallel to the data logging system to identify operational faults and behavioural anomalies effectively.

  10. PLAT: An Automated Fault and Behavioural Anomaly Detection Tool for PLC Controlled Manufacturing Systems

    Directory of Open Access Journals (Sweden)

    Arup Ghosh

    2016-01-01

    Full Text Available Operational faults and behavioural anomalies associated with PLC control processes take place often in a manufacturing system. Real time identification of these operational faults and behavioural anomalies is necessary in the manufacturing industry. In this paper, we present an automated tool, called PLC Log-Data Analysis Tool (PLAT that can detect them by using log-data records of the PLC signals. PLAT automatically creates a nominal model of the PLC control process and employs a novel hash table based indexing and searching scheme to satisfy those purposes. Our experiments show that PLAT is significantly fast, provides real time identification of operational faults and behavioural anomalies, and can execute within a small memory footprint. In addition, PLAT can easily handle a large manufacturing system with a reasonable computing configuration and can be installed in parallel to the data logging system to identify operational faults and behavioural anomalies effectively.

  11. Infrared Thermographic Diagnosis Mechanism for Fault Detection of Ball Bearing under Dynamic Loading Conditions

    International Nuclear Information System (INIS)

    Seo, Jin Ju; Yoon, Hanvit; Kim, Dong Yeon; Hong, Dong Pyo; Kim, Won Tae

    2011-01-01

    Fault detection for dynamic loading conditions of rotational machineries was considered from the contactless, non-destructive infrared thermographic method, rather than the traditional diagnosis method. In this paper, by applying a rotating deep-grooved ball bearing, passive thermographic experiment was performed as an alternative way proceeding the traditional fault monitoring. In addition, the thermographic experiments were compared with the vibration spectrum analysis to evaluate the efficiency of the proposed method. Based on the results, it was concluded the temperature characteristics of the ball bearing under dynamic loading conditions were analyzed thoroughly

  12. Adapting plant measurement data to improve hardware fault detection performance in pressurised water reactors

    International Nuclear Information System (INIS)

    Cilliers, A.C.; Mulder, E.J.

    2012-01-01

    Highlights: ► Attempt was to use available resources at a nuclear plant in a value added fashion. ► Includes plant measurement data and plant training and engineering simulator capabilities. ► Solving the fault masking effect by the distributed control systems in the plant. ► Modelling the effect of inaccuracies in plant models used in the simulators. ► Combination of above resulted in the development of a deterministic fault identifications system. -- Abstract: With the fairly recent adoption of digital control and instrumentation systems in the nuclear industry a lot of research now focus on the development expert fault identification systems. The fault identification systems enable detecting early onset faults of fault causes which allows maintenance planning on the equipment showing signs of deterioration or failure. This includes valve and leaks and small cracks in steam generator tubes usually detected by means of ultrasonic inspection. Detecting faults early during transient operation in NPPs is problematic due to the absence of a reliable reference to compare plant measurements with during transients. The distributed application of control systems operating independently to keep the plant operating within the safe operating boundaries complicates the problem since the control systems would not only operate to reduce the effect of transient disturbances but fault disturbances as well. This paper provides a method to adapt the plant measurements that isolates the control actions on the fault and re-introduces it into the measurement data, thereby improving plant diagnostic performance.

  13. Fault Detection of Reciprocating Compressors using a Model from Principles Component Analysis of Vibrations

    International Nuclear Information System (INIS)

    Ahmed, M; Gu, F; Ball, A D

    2012-01-01

    Traditional vibration monitoring techniques have found it difficult to determine a set of effective diagnostic features due to the high complexity of the vibration signals originating from the many different impact sources and wide ranges of practical operating conditions. In this paper Principal Component Analysis (PCA) is used for selecting vibration feature and detecting different faults in a reciprocating compressor. Vibration datasets were collected from the compressor under baseline condition and five common faults: valve leakage, inter-cooler leakage, suction valve leakage, loose drive belt combined with intercooler leakage and belt loose drive belt combined with suction valve leakage. A model using five PCs has been developed using the baseline data sets and the presence of faults can be detected by comparing the T 2 and Q values from the features of fault vibration signals with corresponding thresholds developed from baseline data. However, the Q -statistic procedure produces a better detection as it can separate the five faults completely.

  14. Detection of ''beading faults'' in welded tubes

    International Nuclear Information System (INIS)

    Mondot, J.

    In the steel tube industry the word ''beading'' refers to a highly localised leak affecting the welded zone. During the pneumatic test its flow rate is generally very low no more than a few thousandths of a mm 3 /second. Detection of such a fault by this test is consequently slow, and those which are choked or at the limit of leakage may escape detection. For greater safety, the tube technician is now using non-destructive testing methods such as eddy-currents and ultrasonics [fr

  15. A combined approach based on MAF analysis and AHP method to fault detection mapping: A case study from a gas field, southwest of Iran

    Science.gov (United States)

    Shakiba, Sima; Asghari, Omid; Khah, Nasser Keshavarz Faraj

    2018-01-01

    A combined geostatitical methodology based on Min/Max Auto-correlation Factor (MAF) analysis and Analytical Hierarchy Process (AHP) is presented to generate a suitable Fault Detection Map (FDM) through seismic attributes. Five seismic attributes derived from a 2D time slice obtained from data related to a gas field located in southwest of Iran are used including instantaneous amplitude, similarity, energy, frequency, and Fault Enhancement Filter (FEF). The MAF analysis is implemented to reduce dimension of input variables, and then AHP method is applied on three obtained de-correlated MAF factors as evidential layer. Three Decision Makers (DMs) are used to construct PCMs for determining weights of selected evidential layer. Finally, weights obtained by AHP were multiplied in normalized valued of each alternative (MAF layers) and the concluded weighted layers were integrated in order to prepare final FDM. Results proved that applying algorithm proposed in this study generate a map more acceptable than the each individual attribute and sharpen the non-surface discontinuities as well as enhancing continuity of detected faults.

  16. [Early warning for various internal faults of GIS based on ultraviolet spectroscopy].

    Science.gov (United States)

    Zhao, Yu; Wang, Xian-pei; Hu, Hong-hong; Dai, Dang-dang; Long, Jia-chuan; Tian, Meng; Zhu, Guo-wei; Huang, Yun-guang

    2015-02-01

    As the basis of accurate diagnosis, fault early-warning of gas insulation switchgear (GIS) focuses on the time-effectiveness and the applicability. It would be significant to research the method of unified early-warning for partial discharge (PD) and overheated faults in GIS. In the present paper, SO2 is proposed as the common and typical by-product. The unified monitoring could be achieved through ultraviolet spectroscopy (UV) detection of SO2. The derivative method and Savitzky-Golay filtering are employed for baseline correction and smoothing. The wavelength range of 290-310 nm is selected for quantitative detection of SO2. Through UV method, the spectral interference of SF6 and other complex by-products, e.g., SOF2 and SOF2, can be avoided and the features of trace SO2 in GIS can be extracted. The detection system is featured by compacted structure, low maintenance and satisfactory suitability in filed surveillance. By conducting SF6 decomposition experiments, including two types of PD faults and the overheated faults between 200-400 degrees C, the feasibility of proposed UV method has been verified. Fourier transform infrared spectroscopy and gas chromatography methods can be used for subsequent fault diagnosis. The different decomposition features in two kinds of faults are confirmed and the diagnosis strategy has been briefly analyzed. The main by-products under PD are SOF2 and SO2F2. The generated SO2 is significantly less than SOF2. More carbonous by-products will be generated when PD involves epoxy. By contrast, when the material of heater is stainless steel, SF6 decomposes at about 300 "C and the main by-products in overheated faults are SO2 and SO2F2. When heated over 350 degrees C, SO2 is generated much faster. SOz content stably increases when the GIS fault lasts. The faults types could be preliminarily identified based on the generation features of SO2.

  17. An SVM-based solution for fault detection in wind turbines.

    Science.gov (United States)

    Santos, Pedro; Villa, Luisa F; Reñones, Aníbal; Bustillo, Andres; Maudes, Jesús

    2015-03-09

    Research into fault diagnosis in machines with a wide range of variable loads and speeds, such as wind turbines, is of great industrial interest. Analysis of the power signals emitted by wind turbines for the diagnosis of mechanical faults in their mechanical transmission chain is insufficient. A successful diagnosis requires the inclusion of accelerometers to evaluate vibrations. This work presents a multi-sensory system for fault diagnosis in wind turbines, combined with a data-mining solution for the classification of the operational state of the turbine. The selected sensors are accelerometers, in which vibration signals are processed using angular resampling techniques and electrical, torque and speed measurements. Support vector machines (SVMs) are selected for the classification task, including two traditional and two promising new kernels. This multi-sensory system has been validated on a test-bed that simulates the real conditions of wind turbines with two fault typologies: misalignment and imbalance. Comparison of SVM performance with the results of artificial neural networks (ANNs) shows that linear kernel SVM outperforms other kernels and ANNs in terms of accuracy, training and tuning times. The suitability and superior performance of linear SVM is also experimentally analyzed, to conclude that this data acquisition technique generates linearly separable datasets.

  18. An SVM-Based Solution for Fault Detection in Wind Turbines

    Directory of Open Access Journals (Sweden)

    Pedro Santos

    2015-03-01

    Full Text Available Research into fault diagnosis in machines with a wide range of variable loads and speeds, such as wind turbines, is of great industrial interest. Analysis of the power signals emitted by wind turbines for the diagnosis of mechanical faults in their mechanical transmission chain is insufficient. A successful diagnosis requires the inclusion of accelerometers to evaluate vibrations. This work presents a multi-sensory system for fault diagnosis in wind turbines, combined with a data-mining solution for the classification of the operational state of the turbine. The selected sensors are accelerometers, in which vibration signals are processed using angular resampling techniques and electrical, torque and speed measurements. Support vector machines (SVMs are selected for the classification task, including two traditional and two promising new kernels. This multi-sensory system has been validated on a test-bed that simulates the real conditions of wind turbines with two fault typologies: misalignment and imbalance. Comparison of SVM performance with the results of artificial neural networks (ANNs shows that linear kernel SVM outperforms other kernels and ANNs in terms of accuracy, training and tuning times. The suitability and superior performance of linear SVM is also experimentally analyzed, to conclude that this data acquisition technique generates linearly separable datasets.

  19. Fault Detection, Isolation and Recovery (FDIR) Portable Liquid Oxygen Hardware Demonstrator

    Science.gov (United States)

    Oostdyk, Rebecca L.; Perotti, Jose M.

    2011-01-01

    The Fault Detection, Isolation and Recovery (FDIR) hardware demonstration will highlight the effort being conducted by Constellation's Ground Operations (GO) to provide the Launch Control System (LCS) with system-level health management during vehicle processing and countdown activities. A proof-of-concept demonstration of the FDIR prototype established the capability of the software to provide real-time fault detection and isolation using generated Liquid Hydrogen data. The FDIR portable testbed unit (presented here) aims to enhance FDIR by providing a dynamic simulation of Constellation subsystems that feed the FDIR software live data based on Liquid Oxygen system properties. The LO2 cryogenic ground system has key properties that are analogous to the properties of an electronic circuit. The LO2 system is modeled using electrical components and an equivalent circuit is designed on a printed circuit board to simulate the live data. The portable testbed is also be equipped with data acquisition and communication hardware to relay the measurements to the FDIR application running on a PC. This portable testbed is an ideal capability to perform FDIR software testing, troubleshooting, training among others.

  20. Study on Fault Diagnosis of Rolling Bearing Based on Time-Frequency Generalized Dimension

    Directory of Open Access Journals (Sweden)

    Yu Yuan

    2015-01-01

    Full Text Available The condition monitoring technology and fault diagnosis technology of mechanical equipment played an important role in the modern engineering. Rolling bearing is the most common component of mechanical equipment which sustains and transfers the load. Therefore, fault diagnosis of rolling bearings has great significance. Fractal theory provides an effective method to describe the complexity and irregularity of the vibration signals of rolling bearings. In this paper a novel multifractal fault diagnosis approach based on time-frequency domain signals was proposed. The method and numerical algorithm of Multi-fractal analysis in time-frequency domain were provided. According to grid type J and order parameter q in algorithm, the value range of J and the cut-off condition of q were optimized based on the effect on the dimension calculation. Simulation experiments demonstrated that the effective signal identification could be complete by multifractal method in time-frequency domain, which is related to the factors such as signal energy and distribution. And the further fault diagnosis experiments of bearings showed that the multifractal method in time-frequency domain can complete the fault diagnosis, such as the fault judgment and fault types. And the fault detection can be done in the early stage of fault. Therefore, the multifractal method in time-frequency domain used in fault diagnosis of bearing is a practicable method.

  1. Hydraulic Pump Fault Diagnosis Control Research Based on PARD-BP Algorithm

    Directory of Open Access Journals (Sweden)

    LV Dongmei

    2014-12-01

    Full Text Available Combining working principle and failure mechanism of RZU2000HM hydraulic press, with its present fault cases being collected, the working principle of the oil pressure and faults phenomenon of the hydraulic power unit –swash-plate axial piston pump were studied with some emphasis, whose faults will directly affect the dynamic performance of the oil pressure and flow. In order to make hydraulic power unit work reliably, PARD-BP (Pruning Algorithm based Random Degree neural network fault algorithm was introduced, with swash-plate axial piston pump’s vibration fault sample data regarded as input, and fault mode matrix regarded as target output, so that PARD-BP algorithm could be trained. In the end, the vibration results were verified by the vibration modal test, and it was shown that the biggest upward peaks of vacuum pump in X-direction, Y-direction and Z- direction have fallen by 30.49 %, 21.13 % and 18.73 % respectively, so that the reliability of the fact that PARD-BP algorithm could be used for the online fault detection and diagnosis of the hydraulic pump was verified.

  2. A study on quantification of unavailability of DPPS with fault tolerant techniques considering fault tolerant techniques' characteristics

    International Nuclear Information System (INIS)

    Kim, B. G.; Kang, H. G.; Kim, H. E.; Seung, P. H.; Kang, H. G.; Lee, S. J.

    2012-01-01

    With the improvement of digital technologies, digital I and C systems have included more various fault tolerant techniques than conventional analog I and C systems have, in order to increase fault detection and to help the system safely perform the required functions in spite of the presence of faults. So, in the reliability evaluation of digital systems, the fault tolerant techniques (FTTs) and their fault coverage must be considered. To consider the effects of FTTs in a digital system, there have been several studies on the reliability of digital model. Therefore, this research based on literature survey attempts to develop a model to evaluate the plant reliability of the digital plant protection system (DPPS) with fault tolerant techniques considering detection and process characteristics and human errors. Sensitivity analysis is performed to ascertain important variables from the fault management coverage and unavailability based on the proposed model

  3. Wavelet Entropy-Based Traction Inverter Open Switch Fault Diagnosis in High-Speed Railways

    Directory of Open Access Journals (Sweden)

    Keting Hu

    2016-03-01

    Full Text Available In this paper, a diagnosis plan is proposed to settle the detection and isolation problem of open switch faults in high-speed railway traction system traction inverters. Five entropy forms are discussed and compared with the traditional fault detection methods, namely, discrete wavelet transform and discrete wavelet packet transform. The traditional fault detection methods cannot efficiently detect the open switch faults in traction inverters because of the low resolution or the sudden change of the current. The performances of Wavelet Packet Energy Shannon Entropy (WPESE, Wavelet Packet Energy Tsallis Entropy (WPETE with different non-extensive parameters, Wavelet Packet Energy Shannon Entropy with a specific sub-band (WPESE3,6, Empirical Mode Decomposition Shannon Entropy (EMDESE, and Empirical Mode Decomposition Tsallis Entropy (EMDETE with non-extensive parameters in detecting the open switch fault are evaluated by the evaluation parameter. Comparison experiments are carried out to select the best entropy form for the traction inverter open switch fault detection. In addition, the DC component is adopted to isolate the failure Isolated Gate Bipolar Transistor (IGBT. The simulation experiments show that the proposed plan can diagnose single and simultaneous open switch faults correctly and timely.

  4. A Novel Mittag-Leffler Kernel Based Hybrid Fault Diagnosis Method for Wheeled Robot Driving System

    Directory of Open Access Journals (Sweden)

    Xianfeng Yuan

    2015-01-01

    presents a novel hybrid fault diagnosis framework based on Mittag-Leffler kernel (ML-kernel support vector machine (SVM and Dempster-Shafer (D-S fusion. Using sensor data sampled under different running conditions, the proposed approach initially establishes multiple principal component analysis (PCA models for fault feature extraction. The fault feature vectors are then applied to train the probabilistic SVM (PSVM classifiers that arrive at a preliminary fault diagnosis. To improve the accuracy of preliminary results, a novel ML-kernel based PSVM classifier is proposed in this paper, and the positive definiteness of the ML-kernel is proved as well. The basic probability assignments (BPAs are defined based on the preliminary fault diagnosis results and their confidence values. Eventually, the final fault diagnosis result is archived by the fusion of the BPAs. Experimental results show that the proposed framework not only is capable of detecting and identifying the faults in the robot driving system, but also has better performance in stability and diagnosis accuracy compared with the traditional methods.

  5. Detection of Naturally Occurring Gear and Bearing Faults in a Helicopter Drivetrain

    Science.gov (United States)

    2014-01-01

    Detection of Naturally Occurring Gear and Bearing Faults in a Helicopter Drivetrain by Kelsen E. LaBerge, Eric C. Ames, and Brian D. Dykas...5066 ARL-TR-6795 January 2014 Detection of Naturally Occurring Gear and Bearing Faults in a Helicopter Drivetrain Kelsen E. LaBerge...ELEMENT NUMBER 6. AUTHOR(S) Kelsen E. LaBerge, Eric C. Ames, and Brian D. Dykas 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER

  6. Dynamic modeling of gearbox faults: A review

    Science.gov (United States)

    Liang, Xihui; Zuo, Ming J.; Feng, Zhipeng

    2018-01-01

    Gearbox is widely used in industrial and military applications. Due to high service load, harsh operating conditions or inevitable fatigue, faults may develop in gears. If the gear faults cannot be detected early, the health will continue to degrade, perhaps causing heavy economic loss or even catastrophe. Early fault detection and diagnosis allows properly scheduled shutdowns to prevent catastrophic failure and consequently result in a safer operation and higher cost reduction. Recently, many studies have been done to develop gearbox dynamic models with faults aiming to understand gear fault generation mechanism and then develop effective fault detection and diagnosis methods. This paper focuses on dynamics based gearbox fault modeling, detection and diagnosis. State-of-art and challenges are reviewed and discussed. This detailed literature review limits research results to the following fundamental yet key aspects: gear mesh stiffness evaluation, gearbox damage modeling and fault diagnosis techniques, gearbox transmission path modeling and method validation. In the end, a summary and some research prospects are presented.

  7. Fault Detection And Diagnosis For Air Conditioners And Heat Pumps Based On Virtual Sensors

    OpenAIRE

    Kim, Woohyun

    2013-01-01

    The primary goal of this research is to develop and demonstrate an integrated, on-line performance monitoring and diagnostic system with low cost sensors for air conditioning and heat pump equipment. Automated fault detection and diagnostics (FDD) has the potential for improving energy efficiency along with reducing service costs and comfort complaints. To achieve this goal, virtual sensors with low cost measurements and simple models were developed to estimate quantities that would be expens...

  8. An Ensemble of HMMs for Cognitive Fault Detection in Distributed Sensor Networks

    OpenAIRE

    Roveri , Manuel; Trovò , Francesco

    2014-01-01

    Part 3: Social Media and Mobile Applications of AI; International audience; Distributed sensor networks working in harsh environmental conditions can suffer from permanent or transient faults affecting the embedded electronics or the sensors. Fault Diagnosis Systems (FDSs) have been widely studied in the literature to detect, isolate, identify, and possibly accommodate faults. Recently introduced cognitive FDSs, which represent a novel generation of FDSs, are characterized by the capability t...

  9. Fault Tolerant Control for Civil Structures Based on LMI Approach

    Directory of Open Access Journals (Sweden)

    Chunxu Qu

    2013-01-01

    Full Text Available The control system may lose the performance to suppress the structural vibration due to the faults in sensors or actuators. This paper designs the filter to perform the fault detection and isolation (FDI and then reforms the control strategy to achieve the fault tolerant control (FTC. The dynamic equation of the structure with active mass damper (AMD is first formulated. Then, an estimated system is built to transform the FDI filter design problem to the static gain optimization problem. The gain is designed to minimize the gap between the estimated system and the practical system, which can be calculated by linear matrix inequality (LMI approach. The FDI filter is finally used to isolate the sensor faults and reform the FTC strategy. The efficiency of FDI and FTC is validated by the numerical simulation of a three-story structure with AMD system with the consideration of sensor faults. The results show that the proposed FDI filter can detect the sensor faults and FTC controller can effectively tolerate the faults and suppress the structural vibration.

  10. Fault Detection and Isolation and Fault Tolerant Control of Wind Turbines Using Set-Valued Observers

    DEFF Research Database (Denmark)

    Casau, Pedro; Rosa, Paulo Andre Nobre; Tabatabaeipour, Seyed Mojtaba

    2012-01-01

    Research on wind turbine Operations & Maintenance (O&M) procedures is critical to the expansion of Wind Energy Conversion systems (WEC). In order to reduce O&M costs and increase the lifespan of the turbine, we study the application of Set-Valued Observers (SVO) to the problem of Fault Detection...... and Isolation (FDI) and Fault Tolerant Control (FTC) of wind turbines, by taking advantage of the recent advances in SVO theory for model invalidation. A simple wind turbine model is presented along with possible faulty scenarios. The FDI algorithm is built on top of the described model, taking into account...

  11. Research on Fault Diagnosis Method Based on Rule Base Neural Network

    Directory of Open Access Journals (Sweden)

    Zheng Ni

    2017-01-01

    Full Text Available The relationship between fault phenomenon and fault cause is always nonlinear, which influences the accuracy of fault location. And neural network is effective in dealing with nonlinear problem. In order to improve the efficiency of uncertain fault diagnosis based on neural network, a neural network fault diagnosis method based on rule base is put forward. At first, the structure of BP neural network is built and the learning rule is given. Then, the rule base is built by fuzzy theory. An improved fuzzy neural construction model is designed, in which the calculated methods of node function and membership function are also given. Simulation results confirm the effectiveness of this method.

  12. Fault diagnostics of dynamic system operation using a fault tree based method

    International Nuclear Information System (INIS)

    Hurdle, E.E.; Bartlett, L.M.; Andrews, J.D.

    2009-01-01

    For conventional systems, their availability can be considerably improved by reducing the time taken to restore the system to the working state when faults occur. Fault identification can be a significant proportion of the time taken in the repair process. Having diagnosed the problem the restoration of the system back to its fully functioning condition can then take place. This paper expands the capability of previous approaches to fault detection and identification using fault trees for application to dynamically changing systems. The technique has two phases. The first phase is modelling and preparation carried out offline. This gathers information on the effects that sub-system failure will have on the system performance. Causes of the sub-system failures are developed in the form of fault trees. The second phase is application. Sensors are installed on the system to provide information about current system performance from which the potential causes can be deduced. A simple system example is used to demonstrate the features of the method. To illustrate the potential for the method to deal with additional system complexity and redundancy, a section from an aircraft fuel system is used. A discussion of the results is provided.

  13. Study of fault diagnosis software design for complex system based on fault tree

    International Nuclear Information System (INIS)

    Yuan Run; Li Yazhou; Wang Jianye; Hu Liqin; Wang Jiaqun; Wu Yican

    2012-01-01

    Complex systems always have high-level reliability and safety requirements, and same does their diagnosis work. As a great deal of fault tree models have been acquired during the design and operation phases, a fault diagnosis method which combines fault tree analysis with knowledge-based technology has been proposed. The prototype of fault diagnosis software has been realized and applied to mobile LIDAR system. (authors)

  14. Fault Diagnosis of Internal Combustion Engine Valve Clearance Using the Impact Commencement Detection Method

    Science.gov (United States)

    Jiang, Zhinong; Wang, Zijia; Zhang, Jinjie

    2017-01-01

    Internal combustion engines (ICEs) are widely used in many important fields. The valve train clearance of an ICE usually exceeds the normal value due to wear or faulty adjustment. This work aims at diagnosing the valve clearance fault based on the vibration signals measured on the engine cylinder heads. The non-stationarity of the ICE operating condition makes it difficult to obtain the nominal baseline, which is always an awkward problem for fault diagnosis. This paper overcomes the problem by inspecting the timing of valve closing impacts, of which the referenced baseline can be obtained by referencing design parameters rather than extraction during healthy conditions. To accurately detect the timing of valve closing impact from vibration signals, we carry out a new method to detect and extract the commencement of the impacts. The results of experiments conducted on a twelve-cylinder ICE test rig show that the approach is capable of extracting the commencement of valve closing impact accurately and using only one feature can give a superior monitoring of valve clearance. With the help of this technique, the valve clearance fault becomes detectable even without the comparison to the baseline, and the changing trend of the clearance could be trackable. PMID:29244722

  15. Fault Diagnosis of Internal Combustion Engine Valve Clearance Using the Impact Commencement Detection Method.

    Science.gov (United States)

    Jiang, Zhinong; Mao, Zhiwei; Wang, Zijia; Zhang, Jinjie

    2017-12-15

    Internal combustion engines (ICEs) are widely used in many important fields. The valve train clearance of an ICE usually exceeds the normal value due to wear or faulty adjustment. This work aims at diagnosing the valve clearance fault based on the vibration signals measured on the engine cylinder heads. The non-stationarity of the ICE operating condition makes it difficult to obtain the nominal baseline, which is always an awkward problem for fault diagnosis. This paper overcomes the problem by inspecting the timing of valve closing impacts, of which the referenced baseline can be obtained by referencing design parameters rather than extraction during healthy conditions. To accurately detect the timing of valve closing impact from vibration signals, we carry out a new method to detect and extract the commencement of the impacts. The results of experiments conducted on a twelve-cylinder ICE test rig show that the approach is capable of extracting the commencement of valve closing impact accurately and using only one feature can give a superior monitoring of valve clearance. With the help of this technique, the valve clearance fault becomes detectable even without the comparison to the baseline, and the changing trend of the clearance could be trackable.

  16. Fault Detection for Non-Gaussian Stochastic Systems with Time-Varying Delay

    Directory of Open Access Journals (Sweden)

    Tao Li

    2013-01-01

    Full Text Available Fault detection (FD for non-Gaussian stochastic systems with time-varying delay is studied. The available information for the addressed problem is the input and the measured output probability density functions (PDFs of the system. In this framework, firstly, by constructing an augmented Lyapunov functional, which involves some slack variables and a tuning parameter, a delay-dependent condition for the existence of FD observer is derived in terms of linear matrix inequality (LMI and the fault can be detected through a threshold. Secondly, in order to improve the detection sensitivity performance, the optimal algorithm is applied to minimize the threshold value. Finally, paper-making process example is given to demonstrate the applicability of the proposed approach.

  17. Data-Driven Method for Wind Turbine Yaw Angle Sensor Zero-Point Shifting Fault Detection

    Directory of Open Access Journals (Sweden)

    Yan Pei

    2018-03-01

    Full Text Available Wind turbine yaw control plays an important role in increasing the wind turbine production and also in protecting the wind turbine. Accurate measurement of yaw angle is the basis of an effective wind turbine yaw controller. The accuracy of yaw angle measurement is affected significantly by the problem of zero-point shifting. Hence, it is essential to evaluate the zero-point shifting error on wind turbines on-line in order to improve the reliability of yaw angle measurement in real time. Particularly, qualitative evaluation of the zero-point shifting error could be useful for wind farm operators to realize prompt and cost-effective maintenance on yaw angle sensors. In the aim of qualitatively evaluating the zero-point shifting error, the yaw angle sensor zero-point shifting fault is firstly defined in this paper. A data-driven method is then proposed to detect the zero-point shifting fault based on Supervisory Control and Data Acquisition (SCADA data. The zero-point shifting fault is detected in the proposed method by analyzing the power performance under different yaw angles. The SCADA data are partitioned into different bins according to both wind speed and yaw angle in order to deeply evaluate the power performance. An indicator is proposed in this method for power performance evaluation under each yaw angle. The yaw angle with the largest indicator is considered as the yaw angle measurement error in our work. A zero-point shifting fault would trigger an alarm if the error is larger than a predefined threshold. Case studies from several actual wind farms proved the effectiveness of the proposed method in detecting zero-point shifting fault and also in improving the wind turbine performance. Results of the proposed method could be useful for wind farm operators to realize prompt adjustment if there exists a large error of yaw angle measurement.

  18. Optimal design of superconducting fault detector for superconductor triggered fault current limiters

    International Nuclear Information System (INIS)

    Yim, S.-W.; Kim, H.-R.; Hyun, O.-B.; Sim, J.; Park, K.B.; Lee, B.W.

    2008-01-01

    We have designed and tested a superconducting fault detector (SFD) for a 22.9 kV superconductor triggered fault current limiters (STFCLs) using Au/YBCO thin films. The SFD is to detect a fault and commutate the current from the primary path to the secondary path of the STFCL. First, quench characteristics of the Au/YBCO thin films were investigated for various faults having different fault duration. The rated voltage of the Au/YBCO thin films was determined from the results, considering the stability of the Au/YBCO elements. Second, the recovery time to superconductivity after quench was measured in each fault case. In addition, the dependence of the recovery characteristics on numbers and dimension of Au/YBCO elements were investigated. Based on the results, a SFD was designed, fabricated and tested. The SFD successfully detected a fault current and carried out the line commutation. Its recovery time was confirmed to be less than 0.5 s, satisfying the reclosing scheme in the Korea Electric Power Corporation (KEPCO)'s power grid

  19. Arc fault detection system

    Science.gov (United States)

    Jha, Kamal N.

    1999-01-01

    An arc fault detection system for use on ungrounded or high-resistance-grounded power distribution systems is provided which can be retrofitted outside electrical switchboard circuits having limited space constraints. The system includes a differential current relay that senses a current differential between current flowing from secondary windings located in a current transformer coupled to a power supply side of a switchboard, and a total current induced in secondary windings coupled to a load side of the switchboard. When such a current differential is experienced, a current travels through a operating coil of the differential current relay, which in turn opens an upstream circuit breaker located between the switchboard and a power supply to remove the supply of power to the switchboard.

  20. Arc fault detection system

    Science.gov (United States)

    Jha, K.N.

    1999-05-18

    An arc fault detection system for use on ungrounded or high-resistance-grounded power distribution systems is provided which can be retrofitted outside electrical switchboard circuits having limited space constraints. The system includes a differential current relay that senses a current differential between current flowing from secondary windings located in a current transformer coupled to a power supply side of a switchboard, and a total current induced in secondary windings coupled to a load side of the switchboard. When such a current differential is experienced, a current travels through a operating coil of the differential current relay, which in turn opens an upstream circuit breaker located between the switchboard and a power supply to remove the supply of power to the switchboard. 1 fig.

  1. Power System Transient Diagnostics Based on Novel Traveling Wave Detection

    Science.gov (United States)

    Hamidi, Reza Jalilzadeh

    Modern electrical power systems demand novel diagnostic approaches to enhancing the system resiliency by improving the state-of-the-art algorithms. The proliferation of high-voltage optical transducers and high time-resolution measurements provide opportunities to develop novel diagnostic methods of very fast transients in power systems. At the same time, emerging complex configuration, such as multi-terminal hybrid transmission systems, limits the applications of the traditional diagnostic methods, especially in fault location and health monitoring. The impedance-based fault-location methods are inefficient for cross-bounded cables, which are widely used for connection of offshore wind farms to the main grid. Thus, this dissertation first presents a novel traveling wave-based fault-location method for hybrid multi-terminal transmission systems. The proposed method utilizes time-synchronized high-sampling voltage measurements. The traveling wave arrival times (ATs) are detected by observation of the squares of wavelet transformation coefficients. Using the ATs, an over-determined set of linear equations are developed for noise reduction, and consequently, the faulty segment is determined based on the characteristics of the provided equation set. Then, the fault location is estimated. The accuracy and capabilities of the proposed fault location method are evaluated and also compared to the existing traveling-wave-based method for a wide range of fault parameters. In order to improve power systems stability, auto-reclosing (AR), single-phase auto-reclosing (SPAR), and adaptive single-phase auto-reclosing (ASPAR) methods have been developed with the final objectives of distinguishing between the transient and permanent faults to clear the transient faults without de-energization of the solid phases. However, the features of the electrical arcs (transient faults) are severely influenced by a number of random parameters, including the convection of the air and plasma

  2. Energy-Efficient Fault-Tolerant Dynamic Event Region Detection in Wireless Sensor Networks

    DEFF Research Database (Denmark)

    Enemark, Hans-Jacob; Zhang, Yue; Dragoni, Nicola

    2015-01-01

    to a hybrid algorithm for dynamic event region detection, such as real-time tracking of chemical leakage regions. Considering the characteristics of the moving away dynamic events, we propose a return back condition for the hybrid algorithm from distributed neighborhood collaboration, in which a node makes......Fault-tolerant event detection is fundamental to wireless sensor network applications. Existing approaches usually adopt neighborhood collaboration for better detection accuracy, while need more energy consumption due to communication. Focusing on energy efficiency, this paper makes an improvement...... its detection decision based on decisions received from its spatial and temporal neighbors, to local non-communicative decision making. The simulation results demonstrate that the improved algorithm does not degrade the detection accuracy of the original algorithm, while it has better energy...

  3. Active Fault Isolation in MIMO Systems

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2014-01-01

    isolation is based directly on the input/output s ignals applied for the fault detection. It is guaranteed that the fault group includes the fault that had occurred in the system. The second step is individual fault isolation in the fault group . Both types of isolation are obtained by applying dedicated......Active fault isolation of parametric faults in closed-loop MIMO system s are considered in this paper. The fault isolation consists of two steps. T he first step is group- wise fault isolation. Here, a group of faults is isolated from other pos sible faults in the system. The group-wise fault...

  4. Study on Instrument Fault Detection using OLM Techniques for PHM Application in NPPs

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Jae Hwan; Park, Gee Yong; Kim, Jung Taek; Hur, Seop [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

    2015-05-15

    The diagnosis system is relatively being mature owing to many research. Among the various models, this paper introduces some On-Line Monitoring (OLM) models for instrument health monitoring and review applicability on NPPs. In recent years, many researchers are being focused on the prognostics which is predicting the future failure of instruments or equipment by using the status monitoring data. By using the prognostic techniques, we can expect a lot of advantages such as ease of control, power optimization, or optimal use of maintenance resources. And we have performed the test for detecting fault of safety-critical instruments and analyzed the fault detection sensitivity for various instrument failure modes using OLM techniques. OLM techniques using data-driven based model such AAKR or AANN can be useful tools for securing integrity of safety-critical instrument that should always keep healthy conditions for the plant safety.

  5. A Design of Finite Memory Residual Generation Filter for Sensor Fault Detection

    Directory of Open Access Journals (Sweden)

    Kim Pyung Soo

    2017-04-01

    Full Text Available In the current paper, a residual generation filter with finite memory structure is proposed for sensor fault detection. The proposed finite memory residual generation filter provides the residual by real-time filtering of fault vector using only the most recent finite measurements and inputs on the window. It is shown that the residual given by the proposed residual generation filter provides the exact fault for noisefree systems. The proposed residual generation filter is specified to the digital filter structure for the amenability to hardware implementation. Finally, to illustrate the capability of the proposed residual generation filter, extensive simulations are performed for the discretized DC motor system with two types of sensor faults, incipient soft bias-type fault and abrupt bias-type fault. In particular, according to diverse noise levels and windows lengths, meaningful simulation results are given for the abrupt bias-type fault.

  6. Robust recurrent neural network modeling for software fault detection and correction prediction

    International Nuclear Information System (INIS)

    Hu, Q.P.; Xie, M.; Ng, S.H.; Levitin, G.

    2007-01-01

    Software fault detection and correction processes are related although different, and they should be studied together. A practical approach is to apply software reliability growth models to model fault detection, and fault correction process is assumed to be a delayed process. On the other hand, the artificial neural networks model, as a data-driven approach, tries to model these two processes together with no assumptions. Specifically, feedforward backpropagation networks have shown their advantages over analytical models in fault number predictions. In this paper, the following approach is explored. First, recurrent neural networks are applied to model these two processes together. Within this framework, a systematic networks configuration approach is developed with genetic algorithm according to the prediction performance. In order to provide robust predictions, an extra factor characterizing the dispersion of prediction repetitions is incorporated into the performance function. Comparisons with feedforward neural networks and analytical models are developed with respect to a real data set

  7. Fault Detection and Diagnosis In Hall-Héroult Cells Based on Individual Anode Current Measurements Using Dynamic Kernel PCA

    Science.gov (United States)

    Yao, Yuchen; Bao, Jie; Skyllas-Kazacos, Maria; Welch, Barry J.; Akhmetov, Sergey

    2018-04-01

    Individual anode current signals in aluminum reduction cells provide localized cell conditions in the vicinity of each anode, which contain more information than the conventionally measured cell voltage and line current. One common use of this measurement is to identify process faults that can cause significant changes in the anode current signals. While this method is simple and direct, it ignores the interactions between anode currents and other important process variables. This paper presents an approach that applies multivariate statistical analysis techniques to individual anode currents and other process operating data, for the detection and diagnosis of local process abnormalities in aluminum reduction cells. Specifically, since the Hall-Héroult process is time-varying with its process variables dynamically and nonlinearly correlated, dynamic kernel principal component analysis with moving windows is used. The cell is discretized into a number of subsystems, with each subsystem representing one anode and cell conditions in its vicinity. The fault associated with each subsystem is identified based on multivariate statistical control charts. The results show that the proposed approach is able to not only effectively pinpoint the problematic areas in the cell, but also assess the effect of the fault on different parts of the cell.

  8. Tacholess order-tracking approach for wind turbine gearbox fault detection

    Institute of Scientific and Technical Information of China (English)

    Yi WANG; Yong XIE; Guanghua XU; Sicong ZHANG; Chenggang HOU

    2017-01-01

    Monitoring of wind turbines under variablespeed operating conditions has become an important issue in recent years.The gearbox of a wind turbine is the most important transmission unit;it generally exhibits complex vibration signatures due to random variations in operating conditions.Spectral analysis is one of the main approaches in vibration signal processing.However,spectral analysis is based on a stationary assumption and thus inapplicable to the fault diagnosis of wind turbines under variable-speed operating conditions.This constraint limits the application of spectral analysis to wind turbine diagnosis in industrial applications.Although order-tracking methods have been proposed for wind turbine fault detection in recent years,current methods are only applicable to cases in which the instantaneous shaft phase is available.For wind turbines with limited structural spaces,collecting phase signals with tachometers or encoders is difficult.In this study,a tacholess order-tracking method for wind turbines is proposed to overcome the limitations of traditional techniques.The proposed method extracts the instantaneous phase from the vibration signal,resamples the signal at equiangular increments,and calculates the order spectrum for wind turbine fault identification.The effectiveness of the proposed method is experimentally validated with the vibration signals of wind turbines.

  9. Tacholess order-tracking approach for wind turbine gearbox fault detection

    Science.gov (United States)

    Wang, Yi; Xie, Yong; Xu, Guanghua; Zhang, Sicong; Hou, Chenggang

    2017-09-01

    Monitoring of wind turbines under variable-speed operating conditions has become an important issue in recent years. The gearbox of a wind turbine is the most important transmission unit; it generally exhibits complex vibration signatures due to random variations in operating conditions. Spectral analysis is one of the main approaches in vibration signal processing. However, spectral analysis is based on a stationary assumption and thus inapplicable to the fault diagnosis of wind turbines under variable-speed operating conditions. This constraint limits the application of spectral analysis to wind turbine diagnosis in industrial applications. Although order-tracking methods have been proposed for wind turbine fault detection in recent years, current methods are only applicable to cases in which the instantaneous shaft phase is available. For wind turbines with limited structural spaces, collecting phase signals with tachometers or encoders is difficult. In this study, a tacholess order-tracking method for wind turbines is proposed to overcome the limitations of traditional techniques. The proposed method extracts the instantaneous phase from the vibration signal, resamples the signal at equiangular increments, and calculates the order spectrum for wind turbine fault identification. The effectiveness of the proposed method is experimentally validated with the vibration signals of wind turbines.

  10. Fault Detection for Shipboard Monitoring – Volterra Kernel and Hammerstein Model Approaches

    DEFF Research Database (Denmark)

    Lajic, Zoran; Blanke, Mogens; Nielsen, Ulrik Dam

    2009-01-01

    In this paper nonlinear fault detection for in-service monitoring and decision support systems for ships will be presented. The ship is described as a nonlinear system, and the stochastic wave elevation and the associated ship responses are conveniently modelled in frequency domain. The transform....... The transformation from time domain to frequency domain has been conducted by use of Volterra theory. The paper takes as an example fault detection of a containership on which a decision support system has been installed....

  11. Direct detection of near-surface faults by migration of back-scattered surface waves

    KAUST Repository

    Yu, Han; Guo, Bowen; Hanafy, Sherif; Lin, Fan-Chi; Schuster, Gerard T.

    2014-01-01

    We show that diffraction stack migration can be used to estimate the distribution of near-surface faults. The assumption is that near-surface faults generate detectable back-scattered surface waves from impinging surface waves. The processing steps

  12. Fault tolerant control of a three-phase three-wire shunt active filter system based on reliability analysis

    Energy Technology Data Exchange (ETDEWEB)

    Poure, P. [Laboratoire d' Instrumentation Electronique de Nancy LIEN, EA 3440, Nancy-Universite, Faculte des Sciences et Techniques, BP 239, 54506 Vandoeuvre Cedex (France); Weber, P.; Theilliol, D. [Centre de Recherche en Automatique de Nancy UMR 7039, Nancy-Universite, CNRS, Faculte des Sciences et Techniques, BP 239, 54506 Vandoeuvre Cedex (France); Saadate, S. [Groupe de Recherches en Electrotechnique et Electronique de Nancy UMR 7037, Nancy-Universite, CNRS, Faculte des Sciences et Techniques, BP 239, 54506 Vandoeuvre Cedex (France)

    2009-02-15

    This paper deals with fault tolerant shunt three-phase three-wire active filter topologies for which reliability is very important in industry applications. The determination of the optimal reconfiguration structure among various ones with or without redundant components is discussed based on reliability criteria. First, the reconfiguration of the inverter is detailed and a fast fault diagnosis method for power semi-conductor or driver fault detection and compensation is presented. This method avoids false fault detection due to power semi-conductors switching. The control architecture and algorithm are studied and a fault tolerant control strategy is considered. Simulation results in open and short circuit cases validate the theoretical study. Finally, the reliability of the studied three-phase three-wire filter shunt active topologies is analyzed to determine the optimal one. (author)

  13. Local Interaction Simulation Approach for Fault Detection in Medical Ultrasonic Transducers

    Directory of Open Access Journals (Sweden)

    Z. Hashemiyan

    2015-01-01

    Full Text Available A new approach is proposed for modelling medical ultrasonic transducers operating in air. The method is based on finite elements and the local interaction simulation approach. The latter leads to significant reductions of computational costs. Transmission and reception properties of the transducer are analysed using in-air reverberation patterns. The proposed approach can help to provide earlier detection of transducer faults and their identification, reducing the risk of misdiagnosis due to poor image quality.

  14. Fault diagnosis of active magnetic bearings based on Gaussian GLRT detector

    DEFF Research Database (Denmark)

    Nagel, Leon; Galeazzi, Roberto; Voigt, Andreas Jauernik

    2016-01-01

    generalized likelihood ratio test is proposed for detecting faults striking the electromagnet. The detector is capable of detecting and isolating the occurrence of faults in e.g. the windings of bearing by tracking changes in the mean value of a Gaussian distribution. The statistical distribution...

  15. Fault Diagnosis and Fault Tolerant Control with Application on a Wind Turbine Low Speed Shaft Encoder

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Sardi, Hector Eloy Sanchez; Escobet, Teressa

    2015-01-01

    tolerant control of wind turbines using a benchmark model. In this paper, the fault diagnosis scheme is improved and integrated with a fault accommodation scheme which enables and disables the individual pitch algorithm based on the fault detection. In this way, the blade and tower loads are not increased...

  16. Entropy-Based Voltage Fault Diagnosis of Battery Systems for Electric Vehicles

    Directory of Open Access Journals (Sweden)

    Peng Liu

    2018-01-01

    Full Text Available The battery is a key component and the major fault source in electric vehicles (EVs. Ensuring power battery safety is of great significance to make the diagnosis more effective and predict the occurrence of faults, for the power battery is one of the core technologies of EVs. This paper proposes a voltage fault diagnosis detection mechanism using entropy theory which is demonstrated in an EV with a multiple-cell battery system during an actual operation situation. The preliminary analysis, after collecting and preprocessing the typical data periods from Operation Service and Management Center for Electric Vehicle (OSMC-EV in Beijing, shows that overvoltage fault for Li-ion batteries cell can be observed from the voltage curves. To further locate abnormal cells and predict faults, an entropy weight method is established to calculate the objective weight, which reduces the subjectivity and improves the reliability. The result clearly identifies the abnormity of cell voltage. The proposed diagnostic model can be used for EV real-time diagnosis without laboratory testing methods. It is more effective than traditional methods based on contrastive analysis.

  17. Observability analysis for model-based fault detection and sensor selection in induction motors

    International Nuclear Information System (INIS)

    Nakhaeinejad, Mohsen; Bryant, Michael D

    2011-01-01

    Sensors in different types and configurations provide information on the dynamics of a system. For a specific task, the question is whether measurements have enough information or whether the sensor configuration can be changed to improve the performance or to reduce costs. Observability analysis may answer the questions. This paper presents a general algorithm of nonlinear observability analysis with application to model-based diagnostics and sensor selection in three-phase induction motors. A bond graph model of the motor is developed and verified with experiments. A nonlinear observability matrix based on Lie derivatives is obtained from state equations. An observability index based on the singular value decomposition of the observability matrix is obtained. Singular values and singular vectors are used to identify the most and least observable configurations of sensors and parameters. A complex step derivative technique is used in the calculation of Jacobians to improve the computational performance of the observability analysis. The proposed algorithm of observability analysis can be applied to any nonlinear system to select the best configuration of sensors for applications of model-based diagnostics, observer-based controller, or to determine the level of sensor redundancy. Observability analysis on induction motors provides various sensor configurations with corresponding observability indices. Results show the redundancy levels for different sensors, and provide a sensor selection guideline for model-based diagnostics, and for observer-based controllers. The results can also be used for sensor fault detection and to improve the reliability of the system by increasing the redundancy level in measurements

  18. Fault Detection and Diagnosis of Railway Point Machines by Sound Analysis

    Science.gov (United States)

    Lee, Jonguk; Choi, Heesu; Park, Daihee; Chung, Yongwha; Kim, Hee-Young; Yoon, Sukhan

    2016-01-01

    Railway point devices act as actuators that provide different routes to trains by driving switchblades from the current position to the opposite one. Point failure can significantly affect railway operations, with potentially disastrous consequences. Therefore, early detection of anomalies is critical for monitoring and managing the condition of rail infrastructure. We present a data mining solution that utilizes audio data to efficiently detect and diagnose faults in railway condition monitoring systems. The system enables extracting mel-frequency cepstrum coefficients (MFCCs) from audio data with reduced feature dimensions using attribute subset selection, and employs support vector machines (SVMs) for early detection and classification of anomalies. Experimental results show that the system enables cost-effective detection and diagnosis of faults using a cheap microphone, with accuracy exceeding 94.1% whether used alone or in combination with other known methods. PMID:27092509

  19. Stator fault detection for multi-phase machines with multiple reference frames transformation

    DEFF Research Database (Denmark)

    Bianchini, Claudio; Fornasiero, Emanuele; Matzen, T.N.

    2009-01-01

    The paper focuses on a new diagnostic index for fault detection of a five-phase permanent-magnet machine. This machine has been designed for fault tolerant applications, and it is characterized by a mutual inductance equal to zero and a high self inductance, in order to limit the short-circuit cu...

  20. Fault tolerant control with torque limitation based on fault mode for ten-phase permanent magnet synchronous motor

    Directory of Open Access Journals (Sweden)

    Guo Hong

    2015-10-01

    Full Text Available This paper proposes a novel fault tolerant control with torque limitation based on the fault mode for the ten-phase permanent magnet synchronous motor (PMSM under various open-circuit and short-circuit fault conditions, which includes the optimal torque control and the torque limitation control based on the fault mode. The optimal torque control is adopted to guarantee the ripple-free electromagnetic torque operation for the ten-phase motor system under the post-fault condition. Furthermore, we systematically analyze the load capacity of the ten-phase motor system under different fault modes. And a torque limitation control approach based on the fault mode is proposed, which was not available earlier. This approach is able to ensure the safety operation of the faulted motor system in long operating time without causing the overheat fault. The simulation result confirms that the proposed fault tolerant control for the ten-phase motor system is able to guarantee the ripple-free electromagnetic torque and the safety operation in long operating time under the normal and fault conditions.

  1. Generalized composite multiscale permutation entropy and Laplacian score based rolling bearing fault diagnosis

    Science.gov (United States)

    Zheng, Jinde; Pan, Haiyang; Yang, Shubao; Cheng, Junsheng

    2018-01-01

    Multiscale permutation entropy (MPE) is a recently proposed nonlinear dynamic method for measuring the randomness and detecting the nonlinear dynamic change of time series and can be used effectively to extract the nonlinear dynamic fault feature from vibration signals of rolling bearing. To solve the drawback of coarse graining process in MPE, an improved MPE method called generalized composite multiscale permutation entropy (GCMPE) was proposed in this paper. Also the influence of parameters on GCMPE and its comparison with the MPE are studied by analyzing simulation data. GCMPE was applied to the fault feature extraction from vibration signal of rolling bearing and then based on the GCMPE, Laplacian score for feature selection and the Particle swarm optimization based support vector machine, a new fault diagnosis method for rolling bearing was put forward in this paper. Finally, the proposed method was applied to analyze the experimental data of rolling bearing. The analysis results show that the proposed method can effectively realize the fault diagnosis of rolling bearing and has a higher fault recognition rate than the existing methods.

  2. Testing of high-impedance fault relays

    Energy Technology Data Exchange (ETDEWEB)

    Nagpal, M. [Powertech Labs., Inc., Surrey, BC (Canada)

    1995-11-01

    A test system and protocol was developed for the testing of high-impedance fault (HIF) detection devices. A technique was established for point-by-point addition of fault and load currents, the resultant was used for testing the performance of the devices in detecting HIFs in the presence of load current. The system used digitized data from recorded faults and normal currents to generate analog test signals for high-impedance fault detection relays. A test apparatus was built with a 10 kHz band-width and playback duration of 30 minutes on 6 output channels for testing purposes. Three devices which have recently become available were tested and their performance was evaluated based on their respective test results.

  3. Automatic Supervision And Fault Detection In PV System By Wireless Sensors With Interfacing By Labview Program

    Directory of Open Access Journals (Sweden)

    Yousra M Abbas

    2015-08-01

    Full Text Available In this work a wireless monitoring system are designed for automatic detection localization fault in photovoltaic system. In order to avoid the use of modeling and simulation of the PV system we detected the fault by monitoring the output of each individual photovoltaic panel connected in the system by Arduino and transmit this data wirelessly to laptop then interface it by LabVIEW program which made comparison between this data and the measured data taking from reference module at the same condition. The proposed method is very simple but effective detecting and diagnosing the main faults of a PV system and was experimentally validated and has demonstrated its effectiveness in the detection and diagnosing of main faults present in the DC side of PV system.

  4. A novel approach for fault detection and classification of the thermocouple sensor in Nuclear Power Plant using Singular Value Decomposition and Symbolic Dynamic Filter

    International Nuclear Information System (INIS)

    Mandal, Shyamapada; Santhi, B.; Sridhar, S.; Vinolia, K.; Swaminathan, P.

    2017-01-01

    Highlights: • A novel approach to classify the fault pattern using data-driven methods. • Application of robust reconstruction method (SVD) to identify the faulty sensor. • Analysing fault pattern for plenty of sensors using SDF with less time complexity. • An efficient data-driven model is designed to the false and missed alarms. - Abstract: A mathematical model with two layers is developed using data-driven methods for thermocouple sensor fault detection and classification in Nuclear Power Plants (NPP). The Singular Value Decomposition (SVD) based method is applied to detect the faulty sensor from a data set of all sensors, at the first layer. In the second layer, the Symbolic Dynamic Filter (SDF) is employed to classify the fault pattern. If SVD detects any false fault, it is also re-evaluated by the SDF, i.e., the model has two layers of checking to balance the false alarms. The proposed fault detection and classification method is compared with the Principal Component Analysis. Two case studies are taken from Fast Breeder Test Reactor (FBTR) to prove the efficiency of the proposed method.

  5. Automatic Fault Recognition of Photovoltaic Modules Based on Statistical Analysis of Uav Thermography

    Science.gov (United States)

    Kim, D.; Youn, J.; Kim, C.

    2017-08-01

    As a malfunctioning PV (Photovoltaic) cell has a higher temperature than adjacent normal cells, we can detect it easily with a thermal infrared sensor. However, it will be a time-consuming way to inspect large-scale PV power plants by a hand-held thermal infrared sensor. This paper presents an algorithm for automatically detecting defective PV panels using images captured with a thermal imaging camera from an UAV (unmanned aerial vehicle). The proposed algorithm uses statistical analysis of thermal intensity (surface temperature) characteristics of each PV module to verify the mean intensity and standard deviation of each panel as parameters for fault diagnosis. One of the characteristics of thermal infrared imaging is that the larger the distance between sensor and target, the lower the measured temperature of the object. Consequently, a global detection rule using the mean intensity of all panels in the fault detection algorithm is not applicable. Therefore, a local detection rule based on the mean intensity and standard deviation range was developed to detect defective PV modules from individual array automatically. The performance of the proposed algorithm was tested on three sample images; this verified a detection accuracy of defective panels of 97 % or higher. In addition, as the proposed algorithm can adjust the range of threshold values for judging malfunction at the array level, the local detection rule is considered better suited for highly sensitive fault detection compared to a global detection rule.

  6. AUTOMATIC FAULT RECOGNITION OF PHOTOVOLTAIC MODULES BASED ON STATISTICAL ANALYSIS OF UAV THERMOGRAPHY

    Directory of Open Access Journals (Sweden)

    D. Kim

    2017-08-01

    Full Text Available As a malfunctioning PV (Photovoltaic cell has a higher temperature than adjacent normal cells, we can detect it easily with a thermal infrared sensor. However, it will be a time-consuming way to inspect large-scale PV power plants by a hand-held thermal infrared sensor. This paper presents an algorithm for automatically detecting defective PV panels using images captured with a thermal imaging camera from an UAV (unmanned aerial vehicle. The proposed algorithm uses statistical analysis of thermal intensity (surface temperature characteristics of each PV module to verify the mean intensity and standard deviation of each panel as parameters for fault diagnosis. One of the characteristics of thermal infrared imaging is that the larger the distance between sensor and target, the lower the measured temperature of the object. Consequently, a global detection rule using the mean intensity of all panels in the fault detection algorithm is not applicable. Therefore, a local detection rule based on the mean intensity and standard deviation range was developed to detect defective PV modules from individual array automatically. The performance of the proposed algorithm was tested on three sample images; this verified a detection accuracy of defective panels of 97 % or higher. In addition, as the proposed algorithm can adjust the range of threshold values for judging malfunction at the array level, the local detection rule is considered better suited for highly sensitive fault detection compared to a global detection rule.

  7. A leakage-free resonance sparse decomposition technique for bearing fault detection in gearboxes

    Science.gov (United States)

    Osman, Shazali; Wang, Wilson

    2018-03-01

    Most of rotating machinery deficiencies are related to defects in rolling element bearings. Reliable bearing fault detection still remains a challenging task, especially for bearings in gearboxes as bearing-defect-related features are nonstationary and modulated by gear mesh vibration. A new leakage-free resonance sparse decomposition (LRSD) technique is proposed in this paper for early bearing fault detection of gearboxes. In the proposed LRSD technique, a leakage-free filter is suggested to remove strong gear mesh and shaft running signatures. A kurtosis and cosine distance measure is suggested to select appropriate redundancy r and quality factor Q. The signal residual is processed by signal sparse decomposition for highpass and lowpass resonance analysis to extract representative features for bearing fault detection. The effectiveness of the proposed technique is verified by a succession of experimental tests corresponding to different gearbox and bearing conditions.

  8. Active fault diagnosis based on stochastic tests

    DEFF Research Database (Denmark)

    Poulsen, Niels Kjølstad; Niemann, Hans Henrik

    2008-01-01

    The focus of this paper is on stochastic change detection applied in connection with active fault diagnosis (AFD). An auxiliary input signal is applied in AFD. This signal injection in the system will in general allow us to obtain a fast change detection/isolation by considering the output...

  9. Fault diagnosis in spur gears based on genetic algorithm and random forest

    Science.gov (United States)

    Cerrada, Mariela; Zurita, Grover; Cabrera, Diego; Sánchez, René-Vinicio; Artés, Mariano; Li, Chuan

    2016-03-01

    There are growing demands for condition-based monitoring of gearboxes, and therefore new methods to improve the reliability, effectiveness, accuracy of the gear fault detection ought to be evaluated. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance of the diagnostic models. On the other hand, random forest classifiers are suitable models in industrial environments where large data-samples are not usually available for training such diagnostic models. The main aim of this research is to build up a robust system for the multi-class fault diagnosis in spur gears, by selecting the best set of condition parameters on time, frequency and time-frequency domains, which are extracted from vibration signals. The diagnostic system is performed by using genetic algorithms and a classifier based on random forest, in a supervised environment. The original set of condition parameters is reduced around 66% regarding the initial size by using genetic algorithms, and still get an acceptable classification precision over 97%. The approach is tested on real vibration signals by considering several fault classes, one of them being an incipient fault, under different running conditions of load and velocity.

  10. A proposed data base system for detection, classification and ...

    African Journals Online (AJOL)

    A proposed data base system for detection, classification and location of fault on electricity company of Ghana electrical distribution system. Isaac Owusu-Nyarko, Mensah-Ananoo Eugine. Abstract. No Abstract. Keywords: database, classification of fault, power, distribution system, SCADA, ECG. Full Text: EMAIL FULL TEXT ...

  11. Fault Detection for Diesel Engine Actuator

    DEFF Research Database (Denmark)

    Blanke, M.; Bøgh, S.A.; Jørgensen, R.B.

    1994-01-01

    Feedback control systems are vulnerable to faults in control loop sensors and actuators, because feedback actions may cause abrupt responses and process damage when faults occur.......Feedback control systems are vulnerable to faults in control loop sensors and actuators, because feedback actions may cause abrupt responses and process damage when faults occur....

  12. Fault detection for nonlinear systems - A standard problem approach

    DEFF Research Database (Denmark)

    Stoustrup, Jakob; Niemann, Hans Henrik

    1998-01-01

    The paper describes a general method for designing (nonlinear) fault detection and isolation (FDI) systems for nonlinear processes. For a rich class of nonlinear systems, a nonlinear FDI system can be designed using convex optimization procedures. The proposed method is a natural extension...

  13. Improved spectral kurtosis with adaptive redundant multiwavelet packet and its applications for rotating machinery fault detection

    International Nuclear Information System (INIS)

    Chen, Jinglong; Zi, Yanyang; He, Zhengjia; Yuan, Jing

    2012-01-01

    Rotating machinery fault detection is significant to avoid serious accidents and huge economic losses effectively. However, due to the vibration signal with the character of non-stationarity and nonlinearity, the detection and extraction of the fault feature turn into a challenging task. Therefore, a novel method called improved spectral kurtosis (ISK) with adaptive redundant multiwavelet packet (ARMP) is proposed for this task. Spectral kurtosis (SK) has been proved to be a powerful tool to detect and characterize the non-stationary signal. To improve the SK in filter limitation and enhance the resolution of spectral analysis as well as match fault feature optimally, the ARMP is introduced into the SK. Moreover, since kurtosis does not reflect the actual trend of periodic impulses, the SK is improved by incorporating an evaluation index called envelope spectrum entropy as supplement. The proposed method is applied to the rolling element bearing and gear fault detection to validate its reliability and effectiveness. Compared with the conventional frequency spectrum, envelope spectrum, original SK and some single wavelet methods, the results indicate that it could improve the accuracy of frequency-band selection and enhance the ability of rotating machinery fault detection. (paper)

  14. Bearings fault detection in helicopters using frequency readjustment and cyclostationary analysis

    Science.gov (United States)

    Girondin, Victor; Pekpe, Komi Midzodzi; Morel, Herve; Cassar, Jean-Philippe

    2013-07-01

    The objective of this paper is to propose a vibration-based automated framework dealing with local faults occurring on bearings in the transmission of a helicopter. The knowledge of the shaft speed and kinematic computation provide theoretical frequencies that reveal deteriorations on the inner and outer races, on the rolling elements or on the cage. In practice, the theoretical frequencies of bearing faults may be shifted. They may also be masked by parasitical frequencies because the numerous noisy vibrations and the complexity of the transmission mechanics make the signal spectrum very profuse. Consequently, detection methods based on the monitoring of the theoretical frequencies may lead to wrong decisions. In order to deal with this drawback, we propose to readjust the fault frequencies from the theoretical frequencies using the redundancy introduced by the harmonics. The proposed method provides the confidence index of the readjusted frequency. Minor variations in shaft speed may induce random jitters. The change of the contact surface or of the transmission path brings also a random component in amplitude and phase. These random components in the signal destroy spectral localization of frequencies and thus hide the fault occurrence in the spectrum. Under the hypothesis that these random signals can be modeled as cyclostationary signals, the envelope spectrum can reveal that hidden patterns. In order to provide an indicator estimating fault severity, statistics are proposed. They make the hypothesis that the harmonics at the readjusted frequency are corrupted with an additive normally distributed noise. In this case, the statistics computed from the spectra are chi-square distributed and a signal-to-noise indicator is proposed. The algorithms are then tested with data from two test benches and from flight conditions. The bearing type and the radial load are the main differences between the experiences on the benches. The fault is mainly visible in the

  15. A geometric approach for fault detection and isolation of stator short circuit failure in a single asynchronous machine

    KAUST Repository

    Khelouat, Samir

    2012-06-01

    This paper deals with the problem of detection and isolation of stator short-circuit failure in a single asynchronous machine using a geometric approach. After recalling the basis of the geometric approach for fault detection and isolation in nonlinear systems, we will study some structural properties which are fault detectability and isolation fault filter existence. We will then design filters for residual generation. We will consider two approaches: a two-filters structure and a single filter structure, both aiming at generating residuals which are sensitive to one fault and insensitive to the other faults. Some numerical tests will be presented to illustrate the efficiency of the method.

  16. A new and accurate fault location algorithm for combined transmission lines using Adaptive Network-Based Fuzzy Inference System

    Energy Technology Data Exchange (ETDEWEB)

    Sadeh, Javad; Afradi, Hamid [Electrical Engineering Department, Faculty of Engineering, Ferdowsi University of Mashhad, P.O. Box: 91775-1111, Mashhad (Iran)

    2009-11-15

    This paper presents a new and accurate algorithm for locating faults in a combined overhead transmission line with underground power cable using Adaptive Network-Based Fuzzy Inference System (ANFIS). The proposed method uses 10 ANFIS networks and consists of 3 stages, including fault type classification, faulty section detection and exact fault location. In the first part, an ANFIS is used to determine the fault type, applying four inputs, i.e., fundamental component of three phase currents and zero sequence current. Another ANFIS network is used to detect the faulty section, whether the fault is on the overhead line or on the underground cable. Other eight ANFIS networks are utilized to pinpoint the faults (two for each fault type). Four inputs, i.e., the dc component of the current, fundamental frequency of the voltage and current and the angle between them, are used to train the neuro-fuzzy inference systems in order to accurately locate the faults on each part of the combined line. The proposed method is evaluated under different fault conditions such as different fault locations, different fault inception angles and different fault resistances. Simulation results confirm that the proposed method can be used as an efficient means for accurate fault location on the combined transmission lines. (author)

  17. Diagnosis and Fault-tolerant Control

    DEFF Research Database (Denmark)

    Blanke, Mogens; Kinnaert, Michel; Lunze, Jan

    the applicability of the presented methods. The theoretical results are illustrated by two running examples which are used throughout the book. The book addresses engineering students, engineers in industry and researchers who wish to get a survey over the variety of approaches to process diagnosis and fault......The book presents effective model-based analysis and design methods for fault diagnosis and fault-tolerant control. Architectural and structural models are used to analyse the propagation of the fault through the process, to test the fault detectability and to find the redundancies in the process...

  18. Consideration of Gyroscopic Effect in Fault Detection and Isolation for Unbalance Excited Rotor Systems

    Directory of Open Access Journals (Sweden)

    Zhentao Wang

    2012-01-01

    Full Text Available Fault detection and isolation (FDI in rotor systems often faces the problem that the system dynamics is dependent on the rotor rotary frequency because of the gyroscopic effect. In unbalance excited rotor systems, the continuously distributed unbalances are hard to be determined or estimated accurately. The unbalance forces as disturbances make fault detection more complicated. The aim of this paper is to develop linear time invariant (LTI FDI methods (i.e., with constant parameters for rotor systems under consideration of gyroscopic effect and disturbances. Two approaches to describe the gyroscopic effect, that is, as unknown inputs and as model uncertainties, are investigated. Based on these two approaches, FDI methods are developed and the results are compared regarding the resulting FDI performances. Results are obtained by the application in a rotor test rig. Restrictions for the application of these methods are discussed.

  19. Model-Based Off-Nominal State Isolation and Detection System for Autonomous Fault Management, Phase II

    Data.gov (United States)

    National Aeronautics and Space Administration — The proposed model-based Fault Management system addresses the need for cost-effective solutions that enable higher levels of onboard spacecraft autonomy to reliably...

  20. Model-Based Off-Nominal State Isolation and Detection System for Autonomous Fault Management, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — The proposed model-based Fault Management system addresses the need for cost-effective solutions that enable higher levels of onboard spacecraft autonomy to reliably...

  1. Fault detection and diagnosis for refrigerator from compressor sensor

    Science.gov (United States)

    Keres, Stephen L.; Gomes, Alberto Regio; Litch, Andrew D.

    2016-12-06

    A refrigerator, a sealed refrigerant system, and method are provided where the refrigerator includes at least a refrigerated compartment and a sealed refrigerant system including an evaporator, a compressor, a condenser, a controller, an evaporator fan, and a condenser fan. The method includes monitoring a frequency of the compressor, and identifying a fault condition in the at least one component of the refrigerant sealed system in response to the compressor frequency. The method may further comprise calculating a compressor frequency rate based upon the rate of change of the compressor frequency, wherein a fault in the condenser fan is identified if the compressor frequency rate is positive and exceeds a condenser fan fault threshold rate, and wherein a fault in the evaporator fan is identified if the compressor frequency rate is negative and exceeds an evaporator fan fault threshold rate.

  2. A Wireless Sensor System for Real-Time Monitoring and Fault Detection of Motor Arrays.

    Science.gov (United States)

    Medina-García, Jonathan; Sánchez-Rodríguez, Trinidad; Galán, Juan Antonio Gómez; Delgado, Aránzazu; Gómez-Bravo, Fernando; Jiménez, Raúl

    2017-02-25

    This paper presents a wireless fault detection system for industrial motors that combines vibration, motor current and temperature analysis, thus improving the detection of mechanical faults. The design also considers the time of detection and further possible actions, which are also important for the early detection of possible malfunctions, and thus for avoiding irreversible damage to the motor. The remote motor condition monitoring is implemented through a wireless sensor network (WSN) based on the IEEE 802.15.4 standard. The deployed network uses the beacon-enabled mode to synchronize several sensor nodes with the coordinator node, and the guaranteed time slot mechanism provides data monitoring with a predetermined latency. A graphic user interface offers remote access to motor conditions and real-time monitoring of several parameters. The developed wireless sensor node exhibits very low power consumption since it has been optimized both in terms of hardware and software. The result is a low cost, highly reliable and compact design, achieving a high degree of autonomy of more than two years with just one 3.3 V/2600 mAh battery. Laboratory and field tests confirm the feasibility of the wireless system.

  3. Fault Location Based on Synchronized Measurements: A Comprehensive Survey

    Science.gov (United States)

    Al-Mohammed, A. H.; Abido, M. A.

    2014-01-01

    This paper presents a comprehensive survey on transmission and distribution fault location algorithms that utilize synchronized measurements. Algorithms based on two-end synchronized measurements and fault location algorithms on three-terminal and multiterminal lines are reviewed. Series capacitors equipped with metal oxide varistors (MOVs), when set on a transmission line, create certain problems for line fault locators and, therefore, fault location on series-compensated lines is discussed. The paper reports the work carried out on adaptive fault location algorithms aiming at achieving better fault location accuracy. Work associated with fault location on power system networks, although limited, is also summarized. Additionally, the nonstandard high-frequency-related fault location techniques based on wavelet transform are discussed. Finally, the paper highlights the area for future research. PMID:24701191

  4. Fault Location Based on Synchronized Measurements: A Comprehensive Survey

    Directory of Open Access Journals (Sweden)

    A. H. Al-Mohammed

    2014-01-01

    Full Text Available This paper presents a comprehensive survey on transmission and distribution fault location algorithms that utilize synchronized measurements. Algorithms based on two-end synchronized measurements and fault location algorithms on three-terminal and multiterminal lines are reviewed. Series capacitors equipped with metal oxide varistors (MOVs, when set on a transmission line, create certain problems for line fault locators and, therefore, fault location on series-compensated lines is discussed. The paper reports the work carried out on adaptive fault location algorithms aiming at achieving better fault location accuracy. Work associated with fault location on power system networks, although limited, is also summarized. Additionally, the nonstandard high-frequency-related fault location techniques based on wavelet transform are discussed. Finally, the paper highlights the area for future research.

  5. Fault detection in IRIS reactor secondary loop using inferential models

    International Nuclear Information System (INIS)

    Perillo, Sergio R.P.; Upadhyaya, Belle R.; Hines, J. Wesley

    2013-01-01

    The development of fault detection algorithms is well-suited for remote deployment of small and medium reactors, such as the IRIS, and the development of new small modular reactors (SMR). However, an extensive number of tests are still to be performed for new engineering aspects and components that are not yet proven technology in the current PWRs, and present some technological challenges for its deployment since many of its features cannot be proven until a prototype plant is built. In this work, an IRIS plant simulation platform was developed using a Simulink® model. The dynamic simulation was utilized in obtaining inferential models that were used to detect faults artificially added to the secondary system simulations. The implementation of data-driven models and the results are discussed. (author)

  6. Design of a multi-model observer-based estimator for Fault Detection and Isolation (FDI strategy: application to a chemical reactor

    Directory of Open Access Journals (Sweden)

    Y. Chetouani

    2008-12-01

    Full Text Available This study presents a FDI strategy for nonlinear dynamic systems. It shows a methodology of tackling the fault detection and isolation issue by combining a technique based on the residuals signal and a technique using the multiple Kalman filters. The usefulness of this combination is the on-line implementation of the set of models, which represents the normal mode and all dynamics of faults, if the statistical decision threshold on the residuals exceeds a fixed value. In other cases, one Extended Kalman Filter (EKF is enough to estimate the process state. After describing the system architecture and the proposed FDI methodology, we present a realistic application in order to show the technique's potential. An algorithm is described and applied to a chemical process like a perfectly stirred chemical reactor functioning in a semi-batch mode. The chemical reaction used is an oxido reduction one, the oxidation of sodium thiosulfate by hydrogen peroxide.

  7. Fan fault diagnosis based on symmetrized dot pattern analysis and image matching

    Science.gov (United States)

    Xu, Xiaogang; Liu, Haixiao; Zhu, Hao; Wang, Songling

    2016-07-01

    To detect the mechanical failure of fans, a new diagnostic method based on the symmetrized dot pattern (SDP) analysis and image matching is proposed. Vibration signals of 13 kinds of running states are acquired on a centrifugal fan test bed and reconstructed by the SDP technique. The SDP pattern templates of each running state are established. An image matching method is performed to diagnose the fault. In order to improve the diagnostic accuracy, the single template, multiple templates and clustering fault templates are used to perform the image matching.

  8. A Framework-Based Approach for Fault-Tolerant Service Robots

    Directory of Open Access Journals (Sweden)

    Heejune Ahn

    2012-11-01

    Full Text Available Recently the component-based approach has become a major trend in intelligent service robot development due to its reusability and productivity. The framework in a component-based system should provide essential services for application components. However, to our knowledge the existing robot frameworks do not yet support fault tolerance service. Moreover, it is often believed that faults can be handled only at the application level. In this paper, by extending the robot framework with the fault tolerance function, we argue that the framework-based fault tolerance approach is feasible and even has many benefits, including that: 1 the system integrators can build fault tolerance applications from non-fault-aware components; 2 the constraints of the components and the operating environment can be considered at the time of integration, which – cannot be anticipated eaily at the time of component development; 3 consistency in system reliability can be obtained even in spite of diverse application component sources. In the proposed construction, we build XML rule files defining the rules for probing and determining the fault conditions of each component, contamination cases from a faulty component, and the possible recovery and safety methods. The rule files are established by a system integrator and the fault manager in the framework controls the fault tolerance process according to the rules. We demonstrate that the fault-tolerant framework can incorporate widely accepted fault tolerance techniques. The effectiveness and real-time performance of the framework-based approach and its techniques are examined by testing an autonomous mobile robot in typical fault scenarios.

  9. Fault Detection of Wind Turbines with Uncertain Parameters

    DEFF Research Database (Denmark)

    Tabatabaeipour, Seyed Mojtaba; Odgaard, Peter Fogh; Bak, Thomas

    2012-01-01

    on the torque coefficient. High noise on the wind speed measurement, nonlinearities in the aerodynamic torque and uncertainties on the parameters make fault detection a challenging problem. We use an effective wind speed estimator to reduce the noise on the wind speed measurements. A set-membership approach...... is detected. For representation of these sets we use zonotopes and for modeling of uncertainties we use matrix zonotopes, which yields a computationally efficient algorithm. The method is applied to the wind turbine benchmark problem without and with uncertainties. The result demonstrates the effectiveness...

  10. An efficient diagnostic technique for distribution systems based on under fault voltages and currents

    Energy Technology Data Exchange (ETDEWEB)

    Campoccia, A.; Di Silvestre, M.L.; Incontrera, I.; Riva Sanseverino, E. [Dipartimento di Ingegneria Elettrica elettronica e delle Telecomunicazioni, Universita degli Studi di Palermo, viale delle Scienze, 90128 Palermo (Italy); Spoto, G. [Centro per la Ricerca Elettronica in Sicilia, Monreale, Via Regione Siciliana 49, 90046 Palermo (Italy)

    2010-10-15

    Service continuity is one of the major aspects in the definition of the quality of the electrical energy, for this reason the research in the field of faults diagnostic for distribution systems is spreading ever more. Moreover the increasing interest around modern distribution systems automation for management purposes gives faults diagnostics more tools to detect outages precisely and in short times. In this paper, the applicability of an efficient fault location and characterization methodology within a centralized monitoring system is discussed. The methodology, appropriate for any kind of fault, is based on the use of the analytical model of the network lines and uses the fundamental components rms values taken from the transient measures of line currents and voltages at the MV/LV substations. The fault location and identification algorithm, proposed by the authors and suitably restated, has been implemented on a microprocessor-based device that can be installed at each MV/LV substation. The speed and precision of the algorithm have been tested against the errors deriving from the fundamental extraction within the prescribed fault clearing times and against the inherent precision of the electronic device used for computation. The tests have been carried out using Matlab Simulink for simulating the faulted system. (author)

  11. Solving fault diagnosis problems linear synthesis techniques

    CERN Document Server

    Varga, Andreas

    2017-01-01

    This book addresses fault detection and isolation topics from a computational perspective. Unlike most existing literature, it bridges the gap between the existing well-developed theoretical results and the realm of reliable computational synthesis procedures. The model-based approach to fault detection and diagnosis has been the subject of ongoing research for the past few decades. While the theoretical aspects of fault diagnosis on the basis of linear models are well understood, most of the computational methods proposed for the synthesis of fault detection and isolation filters are not satisfactory from a numerical standpoint. Several features make this book unique in the fault detection literature: Solution of standard synthesis problems in the most general setting, for both continuous- and discrete-time systems, regardless of whether they are proper or not; consequently, the proposed synthesis procedures can solve a specific problem whenever a solution exists Emphasis on the best numerical algorithms to ...

  12. Discrete Data Qualification System and Method Comprising Noise Series Fault Detection

    Science.gov (United States)

    Fulton, Christopher; Wong, Edmond; Melcher, Kevin; Bickford, Randall

    2013-01-01

    A Sensor Data Qualification (SDQ) function has been developed that allows the onboard flight computers on NASA s launch vehicles to determine the validity of sensor data to ensure that critical safety and operational decisions are not based on faulty sensor data. This SDQ function includes a novel noise series fault detection algorithm for qualification of the output data from LO2 and LH2 low-level liquid sensors. These sensors are positioned in a launch vehicle s propellant tanks in order to detect propellant depletion during a rocket engine s boost operating phase. This detection capability can prevent the catastrophic situation where the engine operates without propellant. The output from each LO2 and LH2 low-level liquid sensor is a discrete valued signal that is expected to be in either of two states, depending on whether the sensor is immersed (wet) or exposed (dry). Conventional methods for sensor data qualification, such as threshold limit checking, are not effective for this type of signal due to its discrete binary-state nature. To address this data qualification challenge, a noise computation and evaluation method, also known as a noise fault detector, was developed to detect unreasonable statistical characteristics in the discrete data stream. The method operates on a time series of discrete data observations over a moving window of data points and performs a continuous examination of the resulting observation stream to identify the presence of anomalous characteristics. If the method determines the existence of anomalous results, the data from the sensor is disqualified for use by other monitoring or control functions.

  13. Observer-based FDI for Gain Fault Detection in Ship Propulsion Benchmark

    DEFF Research Database (Denmark)

    Lootsma, T.F.; Izadi-Zamanabadi, Roozbeh; Nijmeijer, H.

    2001-01-01

    A geometric approach for input-affine nonlinear systems is briefly described and then applied to a ship propulsion benchmark. The obtained results are used to design a diagnostic nonlinear observer for successful FDI of the diesel engine gain fault......A geometric approach for input-affine nonlinear systems is briefly described and then applied to a ship propulsion benchmark. The obtained results are used to design a diagnostic nonlinear observer for successful FDI of the diesel engine gain fault...

  14. Observer-based FDI for Gain Fault Detection in Ship Propulsion Benchmark

    DEFF Research Database (Denmark)

    Lootsma, T.F.; Izadi-Zamanabadi, Roozbeh; Nijmeijer, H.

    2001-01-01

    A geometric approach for input-affine nonlinear systems is briefly described and then applied to a ship propulsion benchmark. The obtained results are used to design a diagnostic nonlinear observer for successful FDI of the diesel engine gain fault.......A geometric approach for input-affine nonlinear systems is briefly described and then applied to a ship propulsion benchmark. The obtained results are used to design a diagnostic nonlinear observer for successful FDI of the diesel engine gain fault....

  15. A Sparsity-Promoted Method Based on Majorization-Minimization for Weak Fault Feature Enhancement.

    Science.gov (United States)

    Ren, Bangyue; Hao, Yansong; Wang, Huaqing; Song, Liuyang; Tang, Gang; Yuan, Hongfang

    2018-03-28

    Fault transient impulses induced by faulty components in rotating machinery usually contain substantial interference. Fault features are comparatively weak in the initial fault stage, which renders fault diagnosis more difficult. In this case, a sparse representation method based on the Majorzation-Minimization (MM) algorithm is proposed to enhance weak fault features and extract the features from strong background noise. However, the traditional MM algorithm suffers from two issues, which are the choice of sparse basis and complicated calculations. To address these challenges, a modified MM algorithm is proposed in which a sparse optimization objective function is designed firstly. Inspired by the Basis Pursuit (BP) model, the optimization function integrates an impulsive feature-preserving factor and a penalty function factor. Second, a modified Majorization iterative method is applied to address the convex optimization problem of the designed function. A series of sparse coefficients can be achieved through iterating, which only contain transient components. It is noteworthy that there is no need to select the sparse basis in the proposed iterative method because it is fixed as a unit matrix. Then the reconstruction step is omitted, which can significantly increase detection efficiency. Eventually, envelope analysis of the sparse coefficients is performed to extract weak fault features. Simulated and experimental signals including bearings and gearboxes are employed to validate the effectiveness of the proposed method. In addition, comparisons are made to prove that the proposed method outperforms the traditional MM algorithm in terms of detection results and efficiency.

  16. Fault detection and identification in missile system guidance and control: a filtering approach

    Science.gov (United States)

    Padgett, Mary Lou; Evers, Johnny; Karplus, Walter J.

    1996-03-01

    Real-world applications of computational intelligence can enhance the fault detection and identification capabilities of a missile guidance and control system. A simulation of a bank-to- turn missile demonstrates that actuator failure may cause the missile to roll and miss the target. Failure of one fin actuator can be detected using a filter and depicting the filter output as fuzzy numbers. The properties and limitations of artificial neural networks fed by these fuzzy numbers are explored. A suite of networks is constructed to (1) detect a fault and (2) determine which fin (if any) failed. Both the zero order moment term and the fin rate term show changes during actuator failure. Simulations address the following questions: (1) How bad does the actuator failure have to be for detection to occur, (2) How bad does the actuator failure have to be for fault detection and isolation to occur, (3) are both zero order moment and fine rate terms needed. A suite of target trajectories are simulated, and properties and limitations of the approach reported. In some cases, detection of the failed actuator occurs within 0.1 second, and isolation of the failure occurs 0.1 after that. Suggestions for further research are offered.

  17. Active fault diagnosis in closed-loop uncertain systems

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik

    2006-01-01

    Fault diagnosis of parametric faults in closed-loop uncertain systems by using an auxiliary input vector is considered in this paper, i.e. active fault diagnosis (AFD). The active fault diagnosis is based directly on the socalled fault signature matrix, related to the YJBK (Youla, Jabr, Bongiorno...... and Kucera) parameterization. Conditions are given for exact detection and isolation of parametric faults in closed-loop uncertain systems....

  18. Rotor cage fault diagnosis in three-phase induction motors based on a current and virtual flux approach

    International Nuclear Information System (INIS)

    Pires, Dulce F.; Pires, V. Fernao; Martins, J.F.; Pires, A.J.

    2009-01-01

    This paper focuses on the detection of a rotor cage fault in a three-phase PWM feed induction motor. In inverter-fed machines there are some difficulties for the detection of a rotor cage fault. These difficulties are due to the fault signature that will be contained in the currents or voltages applied to the machine. In this way, a new approach based on the current and a virtual flux is proposed. The use of the virtual flux allows the improving of the signal to noise ratio. This approach also allows the identification of a rotor cage fault independently of the type of control used in the ac drive. The theoretical principle of this method is discussed. Simulation and experimental results are presented in order to show the effectiveness of the proposed approach

  19. Implementation of fuzzy modeling system for faults detection and diagnosis in three phase induction motor drive system

    Directory of Open Access Journals (Sweden)

    Shorouk Ossama Ibrahim

    2015-05-01

    Full Text Available Induction motors have been intensively utilized in industrial applications, mainly due to their efficiency and reliability. It is necessary that these machines work all the time with its high performance and reliability. So it is necessary to monitor, detect and diagnose different faults that these motors are facing. In this paper an intelligent fault detection and diagnosis for different faults of induction motor drive system is introduced. The stator currents and the time are introduced as inputs to the proposed fuzzy detection and diagnosis system. The direct torque control technique (DTC is adopted as a suitable control technique in the drive system especially, in traction applications, such as Electric Vehicles and Sub-Way Metro that used such a machine. An intelligent modeling technique is adopted as an identifier for different faults; the proposed model introduces the time as an important factor or variable that plays an important role either in fault detection or in decision making for suitable corrective action according to the type of the fault. Experimental results have been obtained to verify the efficiency of the proposed intelligent detector and identifier; a matching between the simulated and experimental results has been noticed.

  20. Fault diagnosis of air conditioning systems based on qualitative bond graph

    International Nuclear Information System (INIS)

    Ghiaus, C.

    1999-01-01

    The bond graph method represents a unified approach for modeling engineering systems. The main idea is that power transfer bonds the components of a system. The bond graph model is the same for both quantitative representation, in which parameters have numerical values, and qualitative approach, in which they are classified qualitatively. To infer the cause of faults using a qualitative method, a system of qualitative equations must be solved. However, the characteristics of qualitative operators require specific methods for solving systems of equations having qualitative variables. This paper proposes both a method for recursively solving the qualitative system of equations derived from bond graph, and a bond graph model of a direct-expansion, mechanical vapor-compression air conditioning system. Results from diagnosing two faults in a real air conditioning system are presented and discussed. Occasionally, more than one fault candidate is inferred for the same set of qualitative values derived from measurements. In these cases, additional information is required to localize the fault. Fault diagnosis is initiated by a fault detection mechanism which also classifies the quantitative measurements into qualitative values; the fault detection is not presented here. (author)

  1. Multi-link faults localization and restoration based on fuzzy fault set for dynamic optical networks.

    Science.gov (United States)

    Zhao, Yongli; Li, Xin; Li, Huadong; Wang, Xinbo; Zhang, Jie; Huang, Shanguo

    2013-01-28

    Based on a distributed method of bit-error-rate (BER) monitoring, a novel multi-link faults restoration algorithm is proposed for dynamic optical networks. The concept of fuzzy fault set (FFS) is first introduced for multi-link faults localization, which includes all possible optical equipment or fiber links with a membership describing the possibility of faults. Such a set is characterized by a membership function which assigns each object a grade of membership ranging from zero to one. OSPF protocol extension is designed for the BER information flooding in the network. The BER information can be correlated to link faults through FFS. Based on the BER information and FFS, multi-link faults localization mechanism and restoration algorithm are implemented and experimentally demonstrated on a GMPLS enabled optical network testbed with 40 wavelengths in each fiber link. Experimental results show that the novel localization mechanism has better performance compared with the extended limited perimeter vector matching (LVM) protocol and the restoration algorithm can improve the restoration success rate under multi-link faults scenario.

  2. Application of learning techniques based on kernel methods for the fault diagnosis in industrial processes

    Directory of Open Access Journals (Sweden)

    Jose M. Bernal-de-Lázaro

    2016-05-01

    Full Text Available This article summarizes the main contributions of the PhD thesis titled: "Application of learning techniques based on kernel methods for the fault diagnosis in Industrial processes". This thesis focuses on the analysis and design of fault diagnosis systems (DDF based on historical data. Specifically this thesis provides: (1 new criteria for adjustment of the kernel methods used to select features with a high discriminative capacity for the fault diagnosis tasks, (2 a proposed approach process monitoring using statistical techniques multivariate that incorporates a reinforced information concerning to the dynamics of the Hotelling's T2 and SPE statistics, whose combination with kernel methods improves the detection of small-magnitude faults; (3 an robustness index to compare the diagnosis classifiers performance taking into account their insensitivity to possible noise and disturbance on historical data.

  3. Torsional vibration signal analysis as a diagnostic tool for planetary gear fault detection

    Science.gov (United States)

    Xue, Song; Howard, Ian

    2018-02-01

    This paper aims to investigate the effectiveness of using the torsional vibration signal as a diagnostic tool for planetary gearbox faults detection. The traditional approach for condition monitoring of the planetary gear uses a stationary transducer mounted on the ring gear casing to measure all the vibration data when the planet gears pass by with the rotation of the carrier arm. However, the time variant vibration transfer paths between the stationary transducer and the rotating planet gear modulate the resultant vibration spectra and make it complex. Torsional vibration signals are theoretically free from this modulation effect and therefore, it is expected to be much easier and more effective to diagnose planetary gear faults using the fault diagnostic information extracted from the torsional vibration. In this paper, a 20 degree of freedom planetary gear lumped-parameter model was developed to obtain the gear dynamic response. In the model, the gear mesh stiffness variations are the main internal vibration generation mechanism and the finite element models were developed for calculation of the sun-planet and ring-planet gear mesh stiffnesses. Gear faults on different components were created in the finite element models to calculate the resultant gear mesh stiffnesses, which were incorporated into the planetary gear model later on to obtain the faulted vibration signal. Some advanced signal processing techniques were utilized to analyses the fault diagnostic results from the torsional vibration. It was found that the planetary gear torsional vibration not only successfully detected the gear fault, but also had the potential to indicate the location of the gear fault. As a result, the planetary gear torsional vibration can be considered an effective alternative approach for planetary gear condition monitoring.

  4. A hybrid fault diagnosis approach based on mixed-domain state features for rotating machinery.

    Science.gov (United States)

    Xue, Xiaoming; Zhou, Jianzhong

    2017-01-01

    To make further improvement in the diagnosis accuracy and efficiency, a mixed-domain state features data based hybrid fault diagnosis approach, which systematically blends both the statistical analysis approach and the artificial intelligence technology, is proposed in this work for rolling element bearings. For simplifying the fault diagnosis problems, the execution of the proposed method is divided into three steps, i.e., fault preliminary detection, fault type recognition and fault degree identification. In the first step, a preliminary judgment about the health status of the equipment can be evaluated by the statistical analysis method based on the permutation entropy theory. If fault exists, the following two processes based on the artificial intelligence approach are performed to further recognize the fault type and then identify the fault degree. For the two subsequent steps, mixed-domain state features containing time-domain, frequency-domain and multi-scale features are extracted to represent the fault peculiarity under different working conditions. As a powerful time-frequency analysis method, the fast EEMD method was employed to obtain multi-scale features. Furthermore, due to the information redundancy and the submergence of original feature space, a novel manifold learning method (modified LGPCA) is introduced to realize the low-dimensional representations for high-dimensional feature space. Finally, two cases with 12 working conditions respectively have been employed to evaluate the performance of the proposed method, where vibration signals were measured from an experimental bench of rolling element bearing. The analysis results showed the effectiveness and the superiority of the proposed method of which the diagnosis thought is more suitable for practical application. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  5. Feature-based handling of surface faults in compact disc players

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Stoustrup, Jakob; Andersen, Palle

    2006-01-01

    In this paper a novel method called feature-based control is presented. The method is designed to improve compact disc players’ handling of surface faults on the discs. The method is based on a fault-tolerant control scheme, which uses extracted features of the surface faults to remove those from...... the detector signals used for control during the occurrence of surface faults. The extracted features are coefficients of Karhunen–Loève approximations of the surface faults. The performance of the feature-based control scheme controlling compact disc players playing discs with surface faults has been...... validated experimentally. The proposed scheme reduces the control errors due to the surface faults, and in some cases where the standard fault handling scheme fails, our scheme keeps the CD-player playing....

  6. Dynamic Output Feedback Based Active Decentralized Fault-Tolerant Control for Reconfigurable Manipulator with Concurrent Failures

    Directory of Open Access Journals (Sweden)

    Yuanchun Li

    2015-01-01

    Full Text Available The goal of this paper is to describe an active decentralized fault-tolerant control (ADFTC strategy based on dynamic output feedback for reconfigurable manipulators with concurrent actuator and sensor failures. Consider each joint module of the reconfigurable manipulator as a subsystem, and treat the fault as the unknown input of the subsystem. Firstly, by virtue of linear matrix inequality (LMI technique, the decentralized proportional-integral observer (DPIO is designed to estimate and compensate the sensor fault online; hereafter, the compensated system model could be derived. Then, the actuator fault is estimated similarly by another DPIO using LMI as well, and the sufficient condition of the existence of H∞ fault-tolerant controller in the dynamic output feedback is presented for the compensated system model. Furthermore, the dynamic output feedback controller is presented based on the estimation of actuator fault to realize active fault-tolerant control. Finally, two 3-DOF reconfigurable manipulators with different configurations are employed to verify the effectiveness of the proposed scheme in simulation. The main advantages of the proposed scheme lie in that it can handle the concurrent faults act on the actuator and sensor on the same joint module, as well as there is no requirement of fault detection and isolation process; moreover, it is more feasible to the modularity of the reconfigurable manipulator.

  7. Application of the Goertzel’s algorithm in the airgap mixed eccentricity fault detection

    Directory of Open Access Journals (Sweden)

    Reljić Dejan

    2015-01-01

    Full Text Available In this paper, a suitable method for the on-line detection of the airgap mixed eccentricity fault in a three-phase cage induction motor has been proposed. The method is based on a Motor Current Signature Analysis (MCSA approach, a technique that is often used for an induction motor condition monitoring and fault diagnosis. It is based on the spectral analysis of the stator line current signal and the frequency identification of specific components, which are created as a result of motor faults. The most commonly used method for the current signal spectral analysis is based on the Fast Fourier transform (FFT. However, due to the complexity and memory demands, the FFT algorithm is not always suitable for real-time systems. Instead of the whole spectrum analysis, this paper suggests only the spectral analysis on the expected airgap fault frequencies employing the Goertzel’s algorithm to predict the magnitude of these frequency components. The method is simple and can be implemented in real-time airgap mixed eccentricity monitoring systems without much computational effort. A low-cost data acquisition system, supported by the LabView software, has been used for the hardware and software implementation of the proposed method. The method has been validated by the laboratory experiments on both the line-connected and the inverter-fed three-phase fourpole cage induction motor operated at the rated frequency and under constant load at a few different values. In addition, the results of the proposed method have been verified through the motor’s vibration signal analysis. [Projekat Ministarstva nauke Republike Srbije, br. III42004

  8. Fault Detection Coverage Quantification of Automatic Test Functions of Digital I and C System in NPPs

    International Nuclear Information System (INIS)

    Choi, Jong Gyun; Lee, Seung Jun; Hur, Seop; Lee, Young Jun; Jang, Seung Cheol

    2011-01-01

    Recently, analog instrument and control (I and C) systems in nuclear power plants (NPPs) have been replaced with digital systems for safer and more efficient operations. Digital I and C systems have adopted various fault-tolerant techniques that help the system correctly and safely perform the specific required functions in spite of the presence of faults. Each fault-tolerant technique has a different inspection period from real-time monitoring to monthly testing. The range covered by each fault-tolerant technique is also different. The digital I and C system, therefore, adopts multiple barriers consisting of various fault-tolerant techniques to increase total fault detection coverage. Even though these fault-tolerant techniques are adopted to ensure and improve the safety of a system, their effects have not been properly considered yet in most PSA models. Therefore, it is necessary to develop an evaluation method that can describe these features of a digital I and C system. Several issues must be considered in the fault coverage estimation of a digital I and C system, and two of them were handled in this work. The first is to quantify the fault coverage of each fault-tolerant technique implemented in the system, and the second is to exclude the duplicated effect of fault-tolerant techniques implemented simultaneously at each level of the system's hierarchy, as a fault occurring in a system might be detected by one or more fault-tolerant techniques. For this work, fault injection experiment was used to obtain the exact relations between faults and multiple barriers of fault-tolerant techniques. This experiment was applied to a bistable processor (BP) of a reactor protection system

  9. Real-Time Fault Detection Approach for Nonlinear Systems and its Asynchronous T-S Fuzzy Observer-Based Implementation.

    Science.gov (United States)

    Li, Linlin; Ding, Steven X; Qiu, Jianbin; Yang, Ying

    2017-02-01

    This paper is concerned with a real-time observer-based fault detection (FD) approach for a general type of nonlinear systems in the presence of external disturbances. To this end, in the first part of this paper, we deal with the definition and the design condition for an L ∞ / L 2 type of nonlinear observer-based FD systems. This analytical framework is fundamental for the development of real-time nonlinear FD systems with the aid of some well-established techniques. In the second part, we address the integrated design of the L ∞ / L 2 observer-based FD systems by applying Takagi-Sugeno (T-S) fuzzy dynamic modeling technique as the solution tool. This fuzzy observer-based FD approach is developed via piecewise Lyapunov functions, and can be applied to the case that the premise variables of the FD system is nonsynchronous with the premise variables of the fuzzy model of the plant. In the end, a case study on the laboratory setup of three-tank system is given to show the efficiency of the proposed results.

  10. Gear Fault Detection Effectiveness as Applied to Tooth Surface Pitting Fatigue Damage

    Science.gov (United States)

    Lewicki, David G.; Dempsey, Paula J.; Heath, Gregory F.; Shanthakumaran, Perumal

    2010-01-01

    A study was performed to evaluate fault detection effectiveness as applied to gear-tooth-pitting-fatigue damage. Vibration and oil-debris monitoring (ODM) data were gathered from 24 sets of spur pinion and face gears run during a previous endurance evaluation study. Three common condition indicators (RMS, FM4, and NA4 [Ed. 's note: See Appendix A-Definitions D were deduced from the time-averaged vibration data and used with the ODM to evaluate their performance for gear fault detection. The NA4 parameter showed to be a very good condition indicator for the detection of gear tooth surface pitting failures. The FM4 and RMS parameters perfomu:d average to below average in detection of gear tooth surface pitting failures. The ODM sensor was successful in detecting a significant 8lDOunt of debris from all the gear tooth pitting fatigue failures. Excluding outliers, the average cumulative mass at the end of a test was 40 mg.

  11. Imaging of Subsurface Faults using Refraction Migration with Fault Flooding

    KAUST Repository

    Metwally, Ahmed Mohsen Hassan

    2017-05-31

    We propose a novel method for imaging shallow faults by migration of transmitted refraction arrivals. The assumption is that there is a significant velocity contrast across the fault boundary that is underlain by a refracting interface. This procedure, denoted as refraction migration with fault flooding, largely overcomes the difficulty in imaging shallow faults with seismic surveys. Numerical results successfully validate this method on three synthetic examples and two field-data sets. The first field-data set is next to the Gulf of Aqaba and the second example is from a seismic profile recorded in Arizona. The faults detected by refraction migration in the Gulf of Aqaba data were in agreement with those indicated in a P-velocity tomogram. However, a new fault is detected at the end of the migration image that is not clearly seen in the traveltime tomogram. This result is similar to that for the Arizona data where the refraction image showed faults consistent with those seen in the P-velocity tomogram, except it also detected an antithetic fault at the end of the line. This fault cannot be clearly seen in the traveltime tomogram due to the limited ray coverage.

  12. Imaging of Subsurface Faults using Refraction Migration with Fault Flooding

    KAUST Repository

    Metwally, Ahmed Mohsen Hassan; Hanafy, Sherif; Guo, Bowen; Kosmicki, Maximillian Sunflower

    2017-01-01

    We propose a novel method for imaging shallow faults by migration of transmitted refraction arrivals. The assumption is that there is a significant velocity contrast across the fault boundary that is underlain by a refracting interface. This procedure, denoted as refraction migration with fault flooding, largely overcomes the difficulty in imaging shallow faults with seismic surveys. Numerical results successfully validate this method on three synthetic examples and two field-data sets. The first field-data set is next to the Gulf of Aqaba and the second example is from a seismic profile recorded in Arizona. The faults detected by refraction migration in the Gulf of Aqaba data were in agreement with those indicated in a P-velocity tomogram. However, a new fault is detected at the end of the migration image that is not clearly seen in the traveltime tomogram. This result is similar to that for the Arizona data where the refraction image showed faults consistent with those seen in the P-velocity tomogram, except it also detected an antithetic fault at the end of the line. This fault cannot be clearly seen in the traveltime tomogram due to the limited ray coverage.

  13. Model-based fault diagnosis in PEM fuel cell systems

    Energy Technology Data Exchange (ETDEWEB)

    Escobet, T; de Lira, S; Puig, V; Quevedo, J [Automatic Control Department (ESAII), Universitat Politecnica de Catalunya (UPC), Rambla Sant Nebridi 10, 08222 Terrassa (Spain); Feroldi, D; Riera, J; Serra, M [Institut de Robotica i Informatica Industrial (IRI), Consejo Superior de Investigaciones Cientificas (CSIC), Universitat Politecnica de Catalunya (UPC) Parc Tecnologic de Barcelona, Edifici U, Carrer Llorens i Artigas, 4-6, Planta 2, 08028 Barcelona (Spain)

    2009-07-01

    In this work, a model-based fault diagnosis methodology for PEM fuel cell systems is presented. The methodology is based on computing residuals, indicators that are obtained comparing measured inputs and outputs with analytical relationships, which are obtained by system modelling. The innovation of this methodology is based on the characterization of the relative residual fault sensitivity. To illustrate the results, a non-linear fuel cell simulator proposed in the literature is used, with modifications, to include a set of fault scenarios proposed in this work. Finally, it is presented the diagnosis results corresponding to these fault scenarios. It is remarkable that with this methodology it is possible to diagnose and isolate all the faults in the proposed set in contrast with other well known methodologies which use the binary signature matrix of analytical residuals and faults. (author)

  14. Acoustic Detection of Faults and Degradation in a High-Bypass Turbofan Engine during VIPR Phase III Testing

    Science.gov (United States)

    Boyle, Devin K.

    2017-01-01

    The Vehicle Integrated Propulsion Research (VIPR) Phase III project was executed at Edwards Air Force Base, California, by the National Aeronautics and Space Administration and several industry, academic, and government partners in the summer of 2015. One of the research objectives was to use external radial acoustic microphone arrays to detect changes in the noise characteristics produced by the research engine during volcanic ash ingestion and seeded fault insertion scenarios involving bleed air valves. Preliminary results indicate the successful acoustic detection of suspected degradation as a result of cumulative exposure to volcanic ash. This detection is shown through progressive changes, particularly in the high-frequency content, as a function of exposure to greater cumulative quantities of ash. Additionally, detection of the simulated failure of the 14th stage stability bleed valve and, to a lesser extent, the station 2.5 stability bleed valve, to their fully-open fail-safe positions was achieved by means of spectral comparisons between nominal (normal valve operation) and seeded fault scenarios.

  15. Using the expected detection delay to assess the performance of different multivariate statistical process monitoring methods for multiplicative and drift faults.

    Science.gov (United States)

    Zhang, Kai; Shardt, Yuri A W; Chen, Zhiwen; Peng, Kaixiang

    2017-03-01

    Using the expected detection delay (EDD) index to measure the performance of multivariate statistical process monitoring (MSPM) methods for constant additive faults have been recently developed. This paper, based on a statistical investigation of the T 2 - and Q-test statistics, extends the EDD index to the multiplicative and drift fault cases. As well, it is used to assess the performance of common MSPM methods that adopt these two test statistics. Based on how to use the measurement space, these methods can be divided into two groups, those which consider the complete measurement space, for example, principal component analysis-based methods, and those which only consider some subspace that reflects changes in key performance indicators, such as partial least squares-based methods. Furthermore, a generic form for them to use T 2 - and Q-test statistics are given. With the extended EDD index, the performance of these methods to detect drift and multiplicative faults is assessed using both numerical simulations and the Tennessee Eastman process. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  16. Fault Detection and Isolation Using Analytical Redundancy Relations for the Ship Propulsion Benchmark

    DEFF Research Database (Denmark)

    Izadi-Zamanabadi, Roozbeh

    The prime objective of Fault-tolerant Control (FTC) systems is to handle faults and discrepancies using appropriate accommodation policies. The issue of obtaining information about various parameters and signals, which have to be monitored for fault detection purposes, becomes a rigorous task...... with the growing number of subsystems. The structural approach, presented in this report, constitutes a general framework for providing information when the system becomes complex. Furthermore, by using this approach, one can determine the calculation sequences of the residuals. The methodology of this approach...

  17. Fuzzy associative memories for instrument fault detection

    International Nuclear Information System (INIS)

    Heger, A.S.

    1996-01-01

    A fuzzy logic instrument fault detection scheme is developed for systems having two or three redundant sensors. In the fuzzy logic approach the deviation between each signal pairing is computed and classified into three fuzzy sets. A rule base is created allowing the human perception of the situation to be represented mathematically. Fuzzy associative memories are then applied. Finally, a defuzzification scheme is used to find the centroid location, and hence the signal status. Real-time analyses are carried out to evaluate the instantaneous signal status as well as the long-term results for the sensor set. Instantaneous signal validation results are used to compute a best estimate for the measured state variable. The long-term sensor validation method uses a frequency fuzzy variable to determine the signal condition over a specific period. To corroborate the methodology synthetic data representing various anomalies are analyzed with both the fuzzy logic technique and the parity space approach. (Author)

  18. Advanced diagnostic system for piston slap faults in IC engines, based on the non-stationary characteristics of the vibration signals

    Science.gov (United States)

    Chen, Jian; Randall, Robert Bond; Peeters, Bart

    2016-06-01

    Artificial Neural Networks (ANNs) have the potential to solve the problem of automated diagnostics of piston slap faults, but the critical issue for the successful application of ANN is the training of the network by a large amount of data in various engine conditions (different speed/load conditions in normal condition, and with different locations/levels of faults). On the other hand, the latest simulation technology provides a useful alternative in that the effect of clearance changes may readily be explored without recourse to cutting metal, in order to create enough training data for the ANNs. In this paper, based on some existing simplified models of piston slap, an advanced multi-body dynamic simulation software was used to simulate piston slap faults with different speeds/loads and clearance conditions. Meanwhile, the simulation models were validated and updated by a series of experiments. Three-stage network systems are proposed to diagnose piston faults: fault detection, fault localisation and fault severity identification. Multi Layer Perceptron (MLP) networks were used in the detection stage and severity/prognosis stage and a Probabilistic Neural Network (PNN) was used to identify which cylinder has faults. Finally, it was demonstrated that the networks trained purely on simulated data can efficiently detect piston slap faults in real tests and identify the location and severity of the faults as well.

  19. A Virtual Sensor for Online Fault Detection of Multitooth-Tools

    Directory of Open Access Journals (Sweden)

    Andres Bustillo

    2011-03-01

    Full Text Available The installation of suitable sensors close to the tool tip on milling centres is not possible in industrial environments. It is therefore necessary to design virtual sensors for these machines to perform online fault detection in many industrial tasks. This paper presents a virtual sensor for online fault detection of multitooth tools based on a Bayesian classifier. The device that performs this task applies mathematical models that function in conjunction with physical sensors. Only two experimental variables are collected from the milling centre that performs the machining operations: the electrical power consumption of the feed drive and the time required for machining each workpiece. The task of achieving reliable signals from a milling process is especially complex when multitooth tools are used, because each kind of cutting insert in the milling centre only works on each workpiece during a certain time window. Great effort has gone into designing a robust virtual sensor that can avoid re-calibration due to, e.g., maintenance operations. The virtual sensor developed as a result of this research is successfully validated under real conditions on a milling centre used for the mass production of automobile engine crankshafts. Recognition accuracy, calculated with a k-fold cross validation, had on average 0.957 of true positives and 0.986 of true negatives. Moreover, measured accuracy was 98%, which suggests that the virtual sensor correctly identifies new cases.

  20. A Virtual Sensor for Online Fault Detection of Multitooth-Tools

    Science.gov (United States)

    Bustillo, Andres; Correa, Maritza; Reñones, Anibal

    2011-01-01

    The installation of suitable sensors close to the tool tip on milling centres is not possible in industrial environments. It is therefore necessary to design virtual sensors for these machines to perform online fault detection in many industrial tasks. This paper presents a virtual sensor for online fault detection of multitooth tools based on a Bayesian classifier. The device that performs this task applies mathematical models that function in conjunction with physical sensors. Only two experimental variables are collected from the milling centre that performs the machining operations: the electrical power consumption of the feed drive and the time required for machining each workpiece. The task of achieving reliable signals from a milling process is especially complex when multitooth tools are used, because each kind of cutting insert in the milling centre only works on each workpiece during a certain time window. Great effort has gone into designing a robust virtual sensor that can avoid re-calibration due to, e.g., maintenance operations. The virtual sensor developed as a result of this research is successfully validated under real conditions on a milling centre used for the mass production of automobile engine crankshafts. Recognition accuracy, calculated with a k-fold cross validation, had on average 0.957 of true positives and 0.986 of true negatives. Moreover, measured accuracy was 98%, which suggests that the virtual sensor correctly identifies new cases. PMID:22163766

  1. Machinery Fault Diagnosis Using Two-Channel Analysis Method Based on Fictitious System Frequency Response Function

    Directory of Open Access Journals (Sweden)

    Kihong Shin

    2015-01-01

    Full Text Available Most existing techniques for machinery health monitoring that utilize measured vibration signals usually require measurement points to be as close as possible to the expected fault components of interest. This is particularly important for implementing condition-based maintenance since the incipient fault signal power may be too small to be detected if a sensor is located further away from the fault source. However, a measurement sensor is often not attached to the ideal point due to geometric or environmental restrictions. In such a case, many of the conventional diagnostic techniques may not be successfully applicable. In this paper, a two-channel analysis method is proposed to overcome such difficulty. It uses two vibration signals simultaneously measured at arbitrary points in a machine. The proposed method is described theoretically by introducing a fictitious system frequency response function. It is then verified experimentally for bearing fault detection. The results show that the suggested method may be a good alternative when ideal points for measurement sensors are not readily available.

  2. Fault Detection and Isolation for a Supermarket Refrigeration System - Part Two

    DEFF Research Database (Denmark)

    Yang, Zhenyu; Rasmussen, Karsten B.; Kieu, Anh T.

    2011-01-01

    be isolated by using a bank of UIOs. Thereby, a complete FDI approach is proposed by combining the Extended-Kalman-Filter (EKF) and UIO methods, after an extensive comparison of KF-, EKF- and UIO-based FDI methods is carried out. The simulation tests show that the complete FDI approach has a good......The Fault Detection and Isolation (FDI) using the Unknown Input Observer (UIO) for a supermarket refrigeration system is investigated. The original system's state $T_{goods}$ (temp. of the goods) is regarded as a system unknown input in this study, so that the FDI decision is not disturbed...

  3. Diagnosis and fault-tolerant control

    CERN Document Server

    Blanke, Mogens; Lunze, Jan; Staroswiecki, Marcel

    2016-01-01

    Fault-tolerant control aims at a gradual shutdown response in automated systems when faults occur. It satisfies the industrial demand for enhanced availability and safety, in contrast to traditional reactions to faults, which bring about sudden shutdowns and loss of availability. The book presents effective model-based analysis and design methods for fault diagnosis and fault-tolerant control. Architectural and structural models are used to analyse the propagation of the fault through the process, to test the fault detectability and to find the redundancies in the process that can be used to ensure fault tolerance. It also introduces design methods suitable for diagnostic systems and fault-tolerant controllers for continuous processes that are described by analytical models of discrete-event systems represented by automata. The book is suitable for engineering students, engineers in industry and researchers who wish to get an overview of the variety of approaches to process diagnosis and fault-tolerant contro...

  4. Data Driven Fault Tolerant Control : A Subspace Approach

    NARCIS (Netherlands)

    Dong, J.

    2009-01-01

    The main stream research on fault detection and fault tolerant control has been focused on model based methods. As far as a model is concerned, changes therein due to faults have to be extracted from measured data. Generally speaking, existing approaches process measured inputs and outputs either by

  5. Stochastic Resonance algorithms to enhance damage detection in bearing faults

    OpenAIRE

    Castiglione Roberto; Garibaldi Luigi; Marchesiello Stefano

    2015-01-01

    Stochastic Resonance is a phenomenon, studied and mainly exploited in telecommunication, which permits the amplification and detection of weak signals by the assistance of noise. The first papers on this technique are dated early 80 s and were developed to explain the periodically recurrent ice ages. Other applications mainly concern neuroscience, biology, medicine and obviously signal analysis and processing. Recently, some researchers have applied the technique for detecting faults in mecha...

  6. Spatial-Temporal Synchrophasor Data Characterization and Analytics in Smart Grid Fault Detection, Identification, and Impact Causal Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Jiang, Huaiguang; Dai, Xiaoxiao; Gao, David Wenzhong; Zhang, Jun Jason; Zhang, Yingchen; Muljadi, Eduard

    2016-09-01

    An approach of big data characterization for smart grids (SGs) and its applications in fault detection, identification, and causal impact analysis is proposed in this paper, which aims to provide substantial data volume reduction while keeping comprehensive information from synchrophasor measurements in spatial and temporal domains. Especially, based on secondary voltage control (SVC) and local SG observation algorithm, a two-layer dynamic optimal synchrophasor measurement devices selection algorithm (OSMDSA) is proposed to determine SVC zones, their corresponding pilot buses, and the optimal synchrophasor measurement devices. Combining the two-layer dynamic OSMDSA and matching pursuit decomposition, the synchrophasor data is completely characterized in the spatial-temporal domain. To demonstrate the effectiveness of the proposed characterization approach, SG situational awareness is investigated based on hidden Markov model based fault detection and identification using the spatial-temporal characteristics generated from the reduced data. To identify the major impact buses, the weighted Granger causality for SGs is proposed to investigate the causal relationship of buses during system disturbance. The IEEE 39-bus system and IEEE 118-bus system are employed to validate and evaluate the proposed approach.

  7. E-core transverse flux machine with integrated fault detection system

    DEFF Research Database (Denmark)

    Rasmussen, Peter Omand; Runólfsson, Gunnar; Thorsdóttir, Thórunn Ágústa

    2011-01-01

    extent also thermal. Since the E-core transverse flux-machine belongs to the family of the SRMs it has unique properties of intervals without current in the windings. By careful investigation of the voltage and current in these intervals a very simple method to detect single and partial turn short...... circuit faults have been developed. For other types of machines the single and partial turn short circuit is very difficult to deal with and requires normally very comprehensive detection and calculation schemes. The developed detection algorithm combined with the E-core transverse flux machine...

  8. Fault Detection and Correction for the Solar Dynamics Observatory Attitude Control System

    Science.gov (United States)

    Starin, Scott R.; Vess, Melissa F.; Kenney, Thomas M.; Maldonado, Manuel D.; Morgenstern, Wendy M.

    2007-01-01

    The Solar Dynamics Observatory is an Explorer-class mission that will launch in early 2009. The spacecraft will operate in a geosynchronous orbit, sending data 24 hours a day to a devoted ground station in White Sands, New Mexico. It will carry a suite of instruments designed to observe the Sun in multiple wavelengths at unprecedented resolution. The Atmospheric Imaging Assembly includes four telescopes with focal plane CCDs that can image the full solar disk in four different visible wavelengths. The Extreme-ultraviolet Variability Experiment will collect time-correlated data on the activity of the Sun's corona. The Helioseismic and Magnetic Imager will enable study of pressure waves moving through the body of the Sun. The attitude control system on Solar Dynamics Observatory is responsible for four main phases of activity. The physical safety of the spacecraft after separation must be guaranteed. Fine attitude determination and control must be sufficient for instrument calibration maneuvers. The mission science mode requires 2-arcsecond control according to error signals provided by guide telescopes on the Atmospheric Imaging Assembly, one of the three instruments to be carried. Lastly, accurate execution of linear and angular momentum changes to the spacecraft must be provided for momentum management and orbit maintenance. In thsp aper, single-fault tolerant fault detection and correction of the Solar Dynamics Observatory attitude control system is described. The attitude control hardware suite for the mission is catalogued, with special attention to redundancy at the hardware level. Four reaction wheels are used where any three are satisfactory. Four pairs of redundant thrusters are employed for orbit change maneuvers and momentum management. Three two-axis gyroscopes provide full redundancy for rate sensing. A digital Sun sensor and two autonomous star trackers provide two-out-of-three redundancy for fine attitude determination. The use of software to maximize

  9. Adaptive Observer-Based Fault-Tolerant Control Design for Uncertain Systems

    Directory of Open Access Journals (Sweden)

    Huaming Qian

    2015-01-01

    Full Text Available This study focuses on the design of the robust fault-tolerant control (FTC system based on adaptive observer for uncertain linear time invariant (LTI systems. In order to improve robustness, rapidity, and accuracy of traditional fault estimation algorithm, an adaptive fault estimation algorithm (AFEA using an augmented observer is presented. By utilizing a new fault estimator model, an improved AFEA based on linear matrix inequality (LMI technique is proposed to increase the performance. Furthermore, an observer-based state feedback fault-tolerant control strategy is designed, which guarantees the stability and performance of the faulty system. Moreover, the adaptive observer and the fault-tolerant controller are designed separately, whose performance can be considered, respectively. Finally, simulation results of an aircraft application are presented to illustrate the effectiveness of the proposed design methods.

  10. Fault Modeling and Testing for Analog Circuits in Complex Space Based on Supply Current and Output Voltage

    Directory of Open Access Journals (Sweden)

    Hongzhi Hu

    2015-01-01

    Full Text Available This paper deals with the modeling of fault for analog circuits. A two-dimensional (2D fault model is first proposed based on collaborative analysis of supply current and output voltage. This model is a family of circle loci on the complex plane, and it simplifies greatly the algorithms for test point selection and potential fault simulations, which are primary difficulties in fault diagnosis of analog circuits. Furthermore, in order to reduce the difficulty of fault location, an improved fault model in three-dimensional (3D complex space is proposed, which achieves a far better fault detection ratio (FDR against measurement error and parametric tolerance. To address the problem of fault masking in both 2D and 3D fault models, this paper proposes an effective design for testability (DFT method. By adding redundant bypassing-components in the circuit under test (CUT, this method achieves excellent fault isolation ratio (FIR in ambiguity group isolation. The efficacy of the proposed model and testing method is validated through experimental results provided in this paper.

  11. Detection and Localization of Tooth Breakage Fault on Wind Turbine Planetary Gear System considering Gear Manufacturing Errors

    Directory of Open Access Journals (Sweden)

    Y. Gui

    2014-01-01

    Full Text Available Sidebands of vibration spectrum are sensitive to the fault degree and have been proved to be useful for tooth fault detection and localization. However, the amplitude and frequency modulation due to manufacturing errors (which are inevitable in actual planetary gear system lead to much more complex sidebands. Thus, in the paper, a lumped parameter model for a typical planetary gear system with various types of errors is established. In the model, the influences of tooth faults on time-varying mesh stiffness and tooth impact force are derived analytically. Numerical methods are then utilized to obtain the response spectra of the system with tooth faults with and without errors. Three system components (including sun, planet, and ring gears with tooth faults are considered in the discussion, respectively. Through detailed comparisons of spectral sidebands, fault characteristic frequencies of the system are acquired. Dynamic experiments on a planetary gear-box test rig are carried out to verify the simulation results and these results are of great significances for the detection and localization of tooth faults in wind turbines.

  12. Robust fault detection in open loop vs. closed loop

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Stoustrup, J.

    1997-01-01

    The robustness aspects of fault detection and isolation (FDI) for uncertain systems are considered. The FDI problem is considered in a standard problem formulation. The FDI design problem is analyzed both in the case where the control input signal is considered as a known external input signal (o...... (open loop) and when the input signal is generated by a feedback controller...

  13. A New Method for Weak Fault Feature Extraction Based on Improved MED

    Directory of Open Access Journals (Sweden)

    Junlin Li

    2018-01-01

    Full Text Available Because of the characteristics of weak signal and strong noise, the low-speed vibration signal fault feature extraction has been a hot spot and difficult problem in the field of equipment fault diagnosis. Moreover, the traditional minimum entropy deconvolution (MED method has been proved to be used to detect such fault signals. The MED uses objective function method to design the filter coefficient, and the appropriate threshold value should be set in the calculation process to achieve the optimal iteration effect. It should be pointed out that the improper setting of the threshold will cause the target function to be recalculated, and the resulting error will eventually affect the distortion of the target function in the background of strong noise. This paper presents an improved MED based method of fault feature extraction from rolling bearing vibration signals that originate in high noise environments. The method uses the shuffled frog leaping algorithm (SFLA, finds the set of optimal filter coefficients, and eventually avoids the artificial error influence of selecting threshold parameter. Therefore, the fault bearing under the two rotating speeds of 60 rpm and 70 rpm is selected for verification with typical low-speed fault bearing as the research object; the results show that SFLA-MED extracts more obvious bearings and has a higher signal-to-noise ratio than the prior MED method.

  14. Short-Circuit Fault Tolerant Control of a Wind Turbine Driven Induction Generator Based on Sliding Mode Observers

    Directory of Open Access Journals (Sweden)

    Takwa Sellami

    2017-10-01

    Full Text Available The installed energy production capacity of wind turbines is growing intensely on a global scale, making the reliability of wind turbine subsystems of greater significance. However, many faults like Inter-Turn Short-Circuit (ITSC may affect the turbine generator and quickly lead to a decline in supplied power quality. In this framework, this paper proposes a Sliding Mode Observer (SMO-based Fault Tolerant Control (FTC scheme for Induction Generator (IG-based variable-speed grid-connected wind turbines. First, the dynamic models of the wind turbine subsystems were developed. The control schemes were elaborated based on the Maximum Power Point Tracking (MPPT method and Indirect Rotor Flux Oriented Control (IRFOC method. The grid control was also established by regulating the active and reactive powers. The performance of the wind turbine system and the stability of injected power to the grid were hence analyzed under both healthy and faulty conditions. The robust developed SMO-based Fault Detection and Isolation (FDI scheme was proved to be fast and efficient for ITSC detection and localization.Afterwards, SMO were involved in scheming the FTC technique. Accordingly, simulation results assert the efficacy of the proposed ITSC FTC method for variable-speed wind turbines with faulty IG in protecting the subsystems from damage and ensuring continuous connection of the wind turbine to the grid during ITSC faults, hence maintaining power quality.

  15. Model-based fault diagnosis approach on external short circuit of lithium-ion battery used in electric vehicles

    International Nuclear Information System (INIS)

    Chen, Zeyu; Xiong, Rui; Tian, Jinpeng; Shang, Xiong; Lu, Jiahuan

    2016-01-01

    Highlights: • The characteristics of ESC fault of lithium-ion battery are investigated experimentally. • The proposed method to simulate the electrical behavior of ESC fault is viable. • Ten parameters in the presented fault model were optimized using a DPSO algorithm. • A two-layer model-based fault diagnosis approach for battery ESC is proposed. • The effective and robustness of the proposed algorithm has been evaluated. - Abstract: This study investigates the external short circuit (ESC) fault characteristics of lithium-ion battery experimentally. An experiment platform is established and the ESC tests are implemented on ten 18650-type lithium cells considering different state-of-charges (SOCs). Based on the experiment results, several efforts have been made. (1) The ESC process can be divided into two periods and the electrical and thermal behaviors within these two periods are analyzed. (2) A modified first-order RC model is employed to simulate the electrical behavior of the lithium cell in the ESC fault process. The model parameters are re-identified by a dynamic-neighborhood particle swarm optimization algorithm. (3) A two-layer model-based ESC fault diagnosis algorithm is proposed. The first layer conducts preliminary fault detection and the second layer gives a precise model-based diagnosis. Four new cells are short-circuited to evaluate the proposed algorithm. It shows that the ESC fault can be diagnosed within 5 s, the error between the model and measured data is less than 0.36 V. The effectiveness of the fault diagnosis algorithm is not sensitive to the precision of battery SOC. The proposed algorithm can still make the correct diagnosis even if there is 10% error in SOC estimation.

  16. Fault detection of feed water treatment process using PCA-WD with parameter optimization.

    Science.gov (United States)

    Zhang, Shirong; Tang, Qian; Lin, Yu; Tang, Yuling

    2017-05-01

    Feed water treatment process (FWTP) is an essential part of utility boilers; and fault detection is expected for its reliability improvement. Classical principal component analysis (PCA) has been applied to FWTPs in our previous work; however, the noises of T 2 and SPE statistics result in false detections and missed detections. In this paper, Wavelet denoise (WD) is combined with PCA to form a new algorithm, (PCA-WD), where WD is intentionally employed to deal with the noises. The parameter selection of PCA-WD is further formulated as an optimization problem; and PSO is employed for optimization solution. A FWTP, sustaining two 1000MW generation units in a coal-fired power plant, is taken as a study case. Its operation data is collected for following verification study. The results show that the optimized WD is effective to restrain the noises of T 2 and SPE statistics, so as to improve the performance of PCA-WD algorithm. And, the parameter optimization enables PCA-WD to get its optimal parameters in an automatic way rather than on individual experience. The optimized PCA-WD is further compared with classical PCA and sliding window PCA (SWPCA), in terms of four cases as bias fault, drift fault, broken line fault and normal condition, respectively. The advantages of the optimized PCA-WD, against classical PCA and SWPCA, is finally convinced with the results. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  17. Multiple incipient sensor faults diagnosis with application to high-speed railway traction devices.

    Science.gov (United States)

    Wu, Yunkai; Jiang, Bin; Lu, Ningyun; Yang, Hao; Zhou, Yang

    2017-03-01

    This paper deals with the problem of incipient fault diagnosis for a class of Lipschitz nonlinear systems with sensor biases and explores further results of total measurable fault information residual (ToMFIR). Firstly, state and output transformations are introduced to transform the original system into two subsystems. The first subsystem is subject to system disturbances and free from sensor faults, while the second subsystem contains sensor faults but without any system disturbances. Sensor faults in the second subsystem are then formed as actuator faults by using a pseudo-actuator based approach. Since the effects of system disturbances on the residual are completely decoupled, multiple incipient sensor faults can be detected by constructing ToMFIR, and the fault detectability condition is then derived for discriminating the detectable incipient sensor faults. Further, a sliding-mode observers (SMOs) based fault isolation scheme is designed to guarantee accurate isolation of multiple sensor faults. Finally, simulation results conducted on a CRH2 high-speed railway traction device are given to demonstrate the effectiveness of the proposed approach. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  18. Model-Based Methods for Fault Diagnosis: Some Guide-Lines

    DEFF Research Database (Denmark)

    Patton, R.J.; Chen, J.; Nielsen, S.B.

    1995-01-01

    This paper provides a review of model-based fault diagnosis techniques. Starting from basic principles, the properties.......This paper provides a review of model-based fault diagnosis techniques. Starting from basic principles, the properties....

  19. Detection of active faults using data fusion techniques : case study, Psachna Island of Evoia, Greece

    NARCIS (Netherlands)

    Gountromichou, Chrysa; Pohl, Christine; Ehlers, Manfred

    2002-01-01

    The identification of active faults (faults potentially capable to trigger an earthquake) is important for a seismically active country like Greece. Remote sensing techniques and GIS analysis were used in order to detect, map and characterize the tectonic structures of Psachna town and the

  20. Analysis of lightning fault detection, location and protection on short and long transmission lines using Real Time Digital Simulation

    Energy Technology Data Exchange (ETDEWEB)

    Oliveira, Andre Luiz Pereira de [Siemens Ltda., Sao Paulo, SP (Brazil)], E-mail: andreluiz.oliveira@siemens.com

    2007-07-01

    The purpose of this paper is to present an analysis of lightning fault detection, location and protection using numeric distance relays applied in high voltage transmission lines, more specifically in the 500 kV transmission lines of CEMIG (Brazilian Energy Utility) between the Vespasiano 2 - Neves 1 (short line - 23.9 km) and Vespasiano 2 - Mesquita (long line - 148.6 km) substations. The analysis was based on the simulations results of numeric distance protective relays on power transmission lines, realized in September 02 to 06, 2002, at Siemens AG's facilities (Erlangen - Germany), using Real Time Digital Simulator (RTDS{sup TM}). Several lightning faults simulations were accomplished, in several conditions of the electrical power system where the protective relays would be installed. The results are presented not only with the times of lightning faults elimination, but also all the functionality of a protection system, including the correct detection, location and other advantages that these modern protection devices make possible to the power system. (author)